1) Diagnostic Identity

Diagnostic Name: Signal Quality

Short Name / Symbol: signal_quality

Diagnostic Class: Signal Integrity / Classification Readiness / Feedback Quality / Sensemaking Input

Primary Function: Estimate the cleanliness, fidelity, reliability, strength, completeness, and interpretive usability of a signal before it is classified, acted upon, stored, amplified, or used for repair.

Primary Use: Determine whether a signal is strong enough and clean enough to support Μ sensemaking, Γ selection, Π constraint design, ℛ repair, U7 memory binding, or gate evaluation.

Core Risk if Ignored: The system may act on noisy, partial, distorted, contaminated, weak, or misread signal, producing misclassification, false attribution, proxy divergence, boundary error, repair misdirection, or memory contamination.

Core Risk if Overtrusted: High-quality signal is mistaken for complete truth, causing the system to ignore localization, context, uncertainty, missing variables, boundary conditions, and later correction.

2) Mechanical Definition

signal_quality measures how clean, reliable, complete, coherent, and usable a signal is as input for interpretation, classification, decision, repair, or memory.

signal_quality answers:

Is this signal good enough to use for the next system move?

A signal may be:

strong but noisy
weak but important
clear but mislocalized
complete but outdated
accurate but context-poor
high-volume but low-fidelity
emotionally intense but causally incomplete
technically precise but meaning-poor

Signal Quality does not ask only whether signal exists. It asks whether the signal can be trusted enough for its intended use.

The same signal can be sufficient for low-risk observation but insufficient for high-impact classification.

Example:

A weak signal may be enough to trigger Ψ attention or Δ testing,
but not enough for hard Γ, irreversible Π, attribution, or U7 memory binding.

3) What the Diagnostic Measures

Direct Measurement Target

signal_quality measures:

• signal clarity
• signal fidelity
• signal strength
• signal reliability
• signal completeness
• signal consistency
• signal freshness
• signal provenance
• signal context
• signal distortion level
• signal noise ratio
• signal confidence support
• signal interpretive usability
• signal relevance to the target question
• signal sufficiency for intended action
• whether signal can support classification or repair
• whether signal can safely enter U7 memory

Indirect / Proxy Signals

signal_quality can be estimated from:

• source reliability
• source proximity
• independent confirmation
• measurement fidelity
• timestamp freshness
• consistency across channels
• contradiction rate
• missing-context rate
• noise level
• compression or summarization level
• distortion incentives
• signal-to-noise ratio
• audit trail quality
• affected-node validation
• recurrence consistency
• stress-test consistency
• whether signal changes under questioning
• whether signal survives cross-layer localization
• whether signal maps to observed behavior

What It Does Not Measure

signal_quality does not directly measure:

• whether the signal is correctly localized
• whether the signal is complete truth
• whether the signal should be acted on immediately
• whether the signal is morally important
• whether the signal is emotionally valid or invalid
• whether the system should ignore weak signals
• whether a classification is correct
• whether a repair pathway is available
• whether memory binding is safe
• whether the signal’s source has full context
• whether a high-quality signal is sufficient for high-impact action

High signal_quality means the signal is cleaner and more usable.

It does not mean the system should skip localization, gate checks, proportionality, or reversibility.

Low signal_quality means the signal is noisy, partial, weak, or contaminated.

It does not mean the signal is irrelevant; low-quality signals may still be early warnings.

4) Canonical State Variables Involved

Canonical state vector:

S = {O, H, ε, ι, Au, µᵢ, BΣ, K, R, Φ}

Primary Variables

• ε: signal often appears first as visible deviation, anomaly, report, error, or disturbance
• Au: signal quality depends on provenance, traceability, and source integrity
• H: hidden debt may appear first through weak or indirect signal
• O: coherence depends on using signals that accurately map to reality
• µᵢ: agent integrity depends on alignment between signal, interpretation, action, and consequence
• R: restoration depends on signals identifying what needs repair

Secondary Variables

• ι: low-quality signal can create false order, false threat, or pseudo-coherence
• BΣ: boundary signals require enough fidelity to preserve consent, refusal, and interface clarity
• K: compatibility judgments require signal that reflects real coupling effects
• Φ: proxy pressure can distort which signals are noticed, amplified, or suppressed

Variables Commonly Confused With signal_quality

5) Localization Signature

Primary Legibility Layers

• U3 — Execution: where signal appears as behavior, output, action, error, deviation, or event
• U4 — Classification / Metrics / Narratives: where signal is interpreted, labeled, measured, or narrativized
• U5 — Coordination / Time: where signal timing, sequence, and latency affect meaning
• U6 — Coherence Field: where signal indicates whole-system coherence or incoherence
• U7 — Memory / Recurrence: where repeated signals reveal pattern or become stored memory
• U8 — Environment / Forcing: where external noise, shocks, or pressure affect signal quality

Primary Leverage Layers

• U2: improve signal permissions, channels, and boundary conditions
• U3: improve observation, measurement, instrumentation, and reporting
• U4: improve categories, definitions, and interpretation filters
• U5: preserve timing, sequence, and recurrence windows
• U7: store signal with provenance and uncertainty

Verification Layers

• U3: did the signal appear in observable behavior or output?
• U4: was it interpreted without distortion?
• U5: was timing preserved?
• U6: does it reflect coherence-field reality or local noise?
• U7: does recurrence support the signal?
• U8: is signal being distorted by external forcing?

Common Mislocalizations

• Treating signal strength as signal accuracy
• Treating emotional intensity as causal completeness
• Treating technical precision as meaning completeness
• Treating high volume as high fidelity
• Treating repeated signal as clean signal without checking source contamination
• Treating absence of signal as absence of condition
• Treating low-quality signal as irrelevant
• Treating early weak signal as proof
• Treating metric signal as reality
• Treating official signal as higher quality by default
• Treating affected-node signal as lower quality by default
• Treating compressed summary as source signal

6) Input Requirements

Required Inputs

To estimate signal_quality, the system needs:

• signal being evaluated
• source of signal
• signal type
• signal timestamp
• signal channel
• signal context
• intended use of signal
• affected variables in S
• provenance quality
• distortion risks
• missing-context indicators
• contradiction or confirmation
• relevant U-layer
• whether signal is direct, inferred, reported, measured, or summarized
• whether signal is sufficient for the proposed next action

Optional Inputs

These improve precision:

• source reliability history
• independent confirmations
• raw source records
• instrumentation details
• measurement error
• compression / summarization history
• signal chain / relay path
• affected-node validation
• environmental noise conditions
• alternative explanations
• recurrence history
• stress-test evidence
• confidence score
• dissenting signal
• channel bias analysis
• prior false positive / false negative history
• source incentives
• temporal sequence around signal emergence

Missing Input Behavior

If signal_quality inputs are missing:

• If source is unknown, treat quality as provisional
• If timestamp is missing, treat freshness as unknown
• If context is missing, avoid hard classification
• If raw signal is unavailable, check for compression distortion
• If provenance is weak, do not bind to U7 memory
• If confirmation is absent, use signal for attention/testing, not closure
• If distortion incentives are unknown, check Φ, rank, AP(t), and X_c(t)
• If affected-node validation is missing, avoid declaring signal complete
• If intended use is high-impact, require higher quality threshold

Default missing-input posture:

treat signal as provisional → preserve source → improve provenance → test localization → delay hard classification

7) Diagnostic States / Ranges

These ranges are qualitative and should be domain-calibrated.

Healthy / Coherence-Supporting Range

Signal is clear, traceable, context-rich, timely, and fit for the intended use.

Signals:

• source is known
• provenance is intact
• context is preserved
• timing is clear
• signal is not over-compressed
• signal is consistent with observed effects
• uncertainty is visible
• contradiction is low or explainable
• affected-node signal is included where relevant
• signal is strong enough for proposed classification or repair
• U7 memory can store signal with scope and provenance

Recommended posture:

Μ interpretation allowed
Γ selection allowed with gate checks
ℛ repair can use signal
U7 memory binding allowed if reversibility conditions are met

Watch Range

Signal is useful but partial, noisy, weak, delayed, compressed, or not yet confirmed.

Signals:

• source is known but context is incomplete
• signal appears through one channel only
• timing is partially unclear
• summary replaces raw source
• contradiction exists
• affected-node validation is missing
• signal may be influenced by Φ, AP(t), or U8 pressure
• signal is enough for attention but not closure
• classification should remain provisional

Recommended posture:

increase Au_eff
seek confirmation
preserve uncertainty
use Δ testing
avoid hard Γ / durable U7 binding

Degraded Range

Signal quality is too poor for strong classification, high-impact action, or repair closure.

Signals:

• provenance is unclear
• source context is missing
• signal is heavily compressed
• contradiction is unresolved
• signal is stale
• channel distortion is likely
• source incentives distort reporting
• emotional, institutional, or metric pressure shapes signal
• signal cannot distinguish cause from symptom
• high confidence is being drawn from weak signal

Recommended posture:

Ψ observation
Au reconstruction
Μ provisional interpretation
Θ certainty damping
signal repair before action

Contraindicated:

hard classification
punitive action
durable U7 memory binding
irreversible Π
repair-complete claims
attribution closure
scaling based on signal

Critical / Collapse-Prone Range

Signal is contaminated, unreliable, unauditable, or actively misleading.

Signals:

• signal is fabricated, captured, or corrupted
• source is unknown or deceptive
• signal chain is broken
• official signal contradicts direct evidence
• noise overwhelms usable information
• signal is weaponized or proxy-driven
• context is irrecoverable
• signal cannot support any meaningful classification
• memory is already binding distorted signal
• action from signal would create major hidden debt

Recommended posture:

stop signal-dependent actuation
preserve remaining evidence
quarantine contaminated signal
restore source access
activate Ξ / Au / FI review
prevent U7 contamination

False Positive Risk

signal_quality may appear high when:

• signal is polished but source-poor
• signal is repeated but copied from same source
• signal aligns with preferred narrative
• signal is high-status
• metric output looks precise
• summary is clean but source is messy
• signal is emotionally compelling
• official confidence is high
• dissenting signal is suppressed
• signal is strong locally but not globally representative

False Negative Risk

signal_quality may appear low when:

• early weak signal is real but incomplete
• affected-node signal lacks formal polish
• signal appears through unfamiliar channels
• signal is noisy because hidden debt is surfacing
• signal challenges preferred Φ
• source lacks status but has proximity
• contradiction reflects partial visibility, not falsehood
• new signal has not yet been instrumented
• signal is symbolic, qualitative, or relational rather than numeric

8) Leading Indicators

signal_quality degradation appears early as:

• source context disappears
• summaries replace raw signal
• confidence rises faster than evidence
• repeated claims trace back to one source
• affected-node signal is excluded
• dissenting signal is dismissed before review
• signal becomes more polished but less traceable
• timing becomes unclear
• classification language appears before evidence stabilizes
• signal is used for broader claims than it supports
• metric signal is treated as full reality
• contradiction is ignored
• weak signal is either over-amplified or suppressed
• noise increases around high-stakes events

9) Lagging Indicators

signal_quality failure has already accumulated debt when:

• wrong classification becomes durable
• repair targets the wrong cause
• attribution is later overturned
• memory stores distorted signal
• hidden debt grows beneath false certainty
• affected nodes reject the official signal record
• repeated decisions fail because input signal was bad
• system must reconstruct source history externally
• legitimacy shock occurs after signal contamination is exposed
• high-confidence action produces incoherent results
• old signal is revealed as proxy-driven, stale, or fabricated

10) Interpretation Rules

How to Read signal_quality

signal_quality should be read as:

context-specific usability of signal for the proposed next action

It is not a universal property of the signal.

A signal may be:

• good enough for attention, not action
• good enough for Δ testing, not closure
• good enough for local repair, not global attribution
• good enough for provisional classification, not U7 binding
• good enough for weak-signal monitoring, not enforcement
• low polish but high proximity
• high precision but low meaning relevance

What Changes Its Meaning

signal_quality changes meaning under:

• low Au_eff
• low EB
• weak FI_integrity
• high AP(t)
• high Φ − O
• high Cv(t)
• high X_c(t)
• low M_int(t)
• high U8 noise
• rank asymmetry
• compression / summarization
• stale timestamps
• weak source provenance
• high consequence severity
• durable classification risk

Context Modifiers

Low Au_eff: signal cannot be traced.

Low EB: key signals may never appear.

Weak FI: bad signal may not be falsified.

High AP(t): signal may be pulled into blame or credit too quickly.

High Φ−O: signals threatening metrics may be filtered or distorted.

High Cv(t): signal may compress before context is preserved.

High X_c(t): signal may be lost in procedural categories.

Low M_int(t): prior signal memory may contaminate current signal.

High consequence severity: required signal quality threshold rises.

Domain Calibration Notes

signal_quality should be calibrated by domain:

• in engineering: logs, error reports, traces, benchmarks, test failures, incident reports
• in AI: user feedback, eval results, retrieval sources, model outputs, memory corrections, tool traces
• in institutions: reports, complaints, audits, metrics, testimony, incident records
• in governance: public data, legal records, testimony, service metrics, policy outcomes
• in relationships: verbal signal, boundary signal, pattern signal, behavioral signal, repair signal
• in archives: source text, summaries, canon notes, glossary entries, cross-links, version history

11) Operator Sequencing Implications

If signal_quality Is Healthy

Allowed with ordinary gate checks:

• Μ can interpret signal
• Γ can select based on signal
• Π can constrain from signal
• ℛ can repair using signal
• Δ can test signal implications
• U7 can store signal with provenance
• FI / HR / Au gates can evaluate the signal pathway

Recommended:

Ψ observe → Au verify → Μ interpret → Γ select → ℛ / Π act → U7 store with provenance

If signal_quality Is Low

Recommended:

Ψ observation → preserve raw signal → Au provenance repair → Μ provisional framing → Δ test → delay hard classification

Or:

treat as weak signal → monitor recurrence → seek confirmation → protect against U7 contamination

Avoid or delay:

• hard Γ
• irreversible Π
• punitive action
• durable U7 memory binding
• attribution closure
• repair-complete claims
• deep ⊗ based on signal
• public certainty
• canonization

Operators Recommended Under Low signal_quality

• Ψ: increase direct observation
• Au: restore traceability and source context
• Μ: keep interpretation provisional
• Θ: damp certainty
• Δ: test signal carefully
• Ξ: check for pseudo-signal, proxy distortion, or inversion
• Γ: select evidence-preserving next steps
• Π: contain risk without overcommitting to classification

Operators Contraindicated Under Low signal_quality

• Γ hard selection: chooses from unreliable input
• Π irreversible constraint: encodes weak signal into structure
• ⊗ deep coupling: spreads signal error across systems
• ⊕ composition: embeds low-quality signal into new identity
• Τ acceleration: outruns verification
• Σ escalation: sacralizes unverified interpretation
• ✕ force: enforces conclusions from weak or contaminated signal

12) Gate Implications

Gates Strengthened By Reliable signal_quality

• FI-Gate: feedback has usable signal fidelity
• Au-Actuation: signal can be traced and inspected before action
• HR-Gate: identity-bound claims are supported only by adequate evidence
• MS-Gate: equivalent signals can be evaluated symmetrically
• ☷ᵢ: principle constraints can respond to real signal rather than noise

Gates Weakened If signal_quality Is Poor or Unknown

If signal_quality is low:

• FI may act on noisy feedback
• Au may trace a poor source but not improve signal quality
• HR may fail if weak signal becomes identity-bound
• MS may compare unequal signals as equivalent
• ☷ᵢ may be invoked from distorted evidence
• Γ may select from noise
• Π may constrain around a misread condition
• ℛ may repair the wrong target

Gate Outcomes Affected

Low signal_quality should push gates toward:

• Pause
• Preserve raw source
• Require provenance
• Require confirmation
• Require localization
• Require reversibility
• Deny hard classification
• Deny durable memory binding
• Deny high-impact actuation
• ∅ for consequence-heavy action based on weak or contaminated signal

13) Scaling Behavior

signal_quality becomes harder to maintain under scale because signals are relayed, summarized, compressed, filtered, incentivized, automated, and interpreted across many layers.

As systems scale:

• raw signal becomes summary
• summaries become dashboards
• dashboards become decision truth
• signal sources become distant from decision nodes
• reporting incentives shape signal
• weak signals are filtered out
• noise increases
• affected-node signal is compressed
• metrics substitute for direct observation
• official signal gains rank authority
• contradictory signal is deprioritized
• signal freshness decays
• provenance becomes harder to preserve
• U7 stores interpreted signal as fact

Scaling Risks

• signal contamination
• dashboard blindness
• metric capture
• weak-signal loss
• source erasure
• stale signal use
• overconfidence from aggregated data
• affected-node invisibility
• false consensus
• low-quality feedback loops
• memory contamination
• wrong repair targeting
• high-rank signal privilege
• official signal over-trust
• noisy channel overload

Scaling Requirements

To scale signal_quality safely, systems need:

• source preservation
• provenance tracking
• raw-to-summary traceability
• timestamp discipline
• independent confirmation pathways
• affected-node signal access
• weak-signal protection
• contradiction handling
• signal-to-noise management
• context preservation
• rank-symmetry checks
• confidence labeling
• classification reversibility
• memory-binding thresholds
• audit sampling
• feedback falsification pathways

Scaling Rule

Signal quality must scale with consequence severity, classification durability, memory persistence, and action irreversibility.

Sanity constraint:

Low signal_quality + high consequence ⇒ gate failure risk ↑

If the signal is low quality and the consequence is high, the system should delay hard action or require stronger verification.

Second constraint:

Low signal_quality + durable U7 binding ⇒ memory contamination risk ↑

If low-quality signal becomes durable memory, future interpretation and repair become distorted.

Third constraint:

Low signal_quality + high AP(t) ⇒ misattribution risk ↑

If signal is poor and attribution pressure is high, blame, credit, or responsibility assignment becomes unstable.

14) Interaction / Coupling Behavior

signal_quality reveals whether a relation, institution, interface, archive, or coupled system has enough clean signal to interact coherently.

What It Reveals About Coupling

• whether one node is reacting to real signal or noise
• whether signal is being distorted across the interface
• whether weak signals can be validated before escalation
• whether boundary signals are clear enough to support repair
• whether compatibility is being judged from accurate interaction data
• whether one node’s filtered signal becomes another node’s false reality
• whether coupling amplifies noise or clarifies signal

What It Reveals About Boundary Integrity

Boundary integrity depends on accurate signal.

When signal_quality is low:

• refusal may be misread
• consent may be over-inferred
• boundary strain may be missed
• support may be misdirected
• crossing may be judged incorrectly
• violations may be overcalled or undercalled
• BΣ repair may target the wrong issue

What It Reveals About Compatibility

Compatibility requires reliable signal exchange.

A coupling may be unsafe if:

each node receives low-quality signal from the other but treats it as high-certainty truth

or:

signal distortion across the interface creates recurring misclassification

Healthy coupling requires not just communication volume, but signal fidelity.

Relevant Interface Acts

• ↺ Reflection: checks whether signal was received accurately
• ⇩ Relaxation: reduces pressure so signal can clarify
• ⊘ Attenuation: reduces coupling when signal is noisy or harmful
• ⊙ Alignment: verifies one’s own signal before transmitting
• →? Invitation: asks for signal without forcing interpretation
• ⚕︎ Restorative Override: requires strong post-action signal audit
• ✕ Force: dangerous when signal is low-quality or mislocalized

15) Failure Modes Detected

Primary Failure Modes

signal_quality detects or predicts:

• noisy input
• weak-signal loss
• signal contamination
• false classification
• misrepair
• misattribution
• memory contamination
• dashboard blindness
• proxy distortion
• affected-node invisibility
• source erasure
• false consensus
• stale signal use
• overconfidence
• distorted boundary reading
• high-confidence error
• signal compression collapse
• official signal over-trust

Composite Regimes Where signal_quality Matters

• Goodhart Collapse: proxy signal replaces reality
• Crisis Loop: bad signal prevents correct repair
• Pseudo-Coherent Basin: poor signal supports apparent order
• Compression Collapse: signal is compressed until meaning is lost
• Taboo Lock: certain signals cannot be evaluated
• Mission Lock: trajectory filters signals that challenge direction
• Coercive Fusion: one node’s signal is overwritten by another’s interpretation
• LOS: latent operation differs from official signal
• Repair Theater: repair claims are built from low-quality signal

16) Accountability & Reintegration Implications

If signal_quality Was Ignored

Likely consequences:

• action was taken from weak evidence
• classification was premature
• repair targeted wrong cause
• memory stored distorted signal
• affected-node signal was excluded or misread
• attribution pressure used noisy input
• confidence exceeded evidence
• hidden debt accumulated
• boundary decisions were based on poor signal
• official record became unreliable

Accountability questions:

• What was the signal?
• Where did it come from?
• How clean was it?
• Was it raw, summarized, inferred, or repeated?
• Was it sufficient for the action taken?
• Was uncertainty preserved?
• Was affected-node signal included?
• Was signal localized correctly?
• Did the signal survive verification?
• Did later recurrence validate or contradict it?
• Was the signal used beyond its quality range?

If signal_quality Was Misread

Possible misread forms:

• weak signal dismissed as irrelevant
• noisy signal treated as false
• high-status signal treated as high quality
• polished signal treated as reliable
• affected-node signal treated as biased by default
• metric signal treated as full reality
• repeated signal treated as independent confirmation
• emotional intensity treated as causal certainty
• technical precision treated as contextual completeness
• low-volume signal treated as low importance

Required Restoration

When signal_quality failure is found:

identify signal source
→ recover raw signal
→ reconstruct provenance
→ separate signal / interpretation / classification
→ check localization
→ correct memory if needed
→ retest through independent channels
→ repair action or classification based on corrected signal

If low-quality signal created unequal consequence, MS-Gate should review who was harmed, blamed, excluded, or burdened by the misread signal.

17) Cross-Domain Examples

Technical / Engineering

A system alert fires repeatedly, but the alert is noisy and does not map cleanly to root cause. Teams begin patching symptoms.

Diagnostic implication: signal exists, but signal_quality is too low for root-cause repair.

Operator sequence: raw log audit → signal filtering → localization → Δ test → ℛ correct layer.

Institutional / Governance

A leadership team relies on summarized reports that omit affected-node experience. Official metrics look stable, but operational strain grows.

Diagnostic implication: polished signal lacks essential context.

Operator sequence: source-to-summary audit → affected-node signal inclusion → FI repair → Γ priority recalibration.

AI / Algorithmic

A model evaluation score suggests improvement, but the dataset is narrow, stale, or overfit.

Diagnostic implication: evaluation signal_quality is insufficient for broad deployment claims.

Operator sequence: eval provenance review → Δ edge-case testing → metric redesign → U7 evaluation memory update.

Interaction / Relational

A short message is interpreted as hostility, but the signal lacks tone, context, timing, and follow-up.

Diagnostic implication: signal_quality is too low for strong attribution or boundary conclusion.

Operator sequence: ↺ reflection → ask for clarification → Θ certainty damping → preserve provisional interpretation.

Archive / Framework Design

A short summary of a prior module is used to define a canon term, but the summary lacks source nuance and creates drift.

Diagnostic implication: compressed source signal is insufficient for canon binding.

Operator sequence: source retrieval → glossary repair → canon-status review → U7 provenance update.

18) Test Protocols

1. Source Test

Can the signal be traced to a source?

Failure signal: signal circulates without origin.

2. Raw Signal Test

Can the system inspect raw signal before summary or interpretation?

Failure signal: only compressed signal remains.

3. Context Test

Does the signal preserve enough context to be interpreted?

Failure signal: signal is technically present but meaning-poor.

4. Independence Test

Are confirmations independent?

Failure signal: multiple reports derive from one source.

5. Freshness Test

Is the signal current enough for the decision?

Failure signal: stale signal guides present action.

6. Noise Test

Is the signal distinguishable from noise?

Failure signal: signal cannot support classification.

7. Contradiction Test

Are contradictions investigated?

Failure signal: contrary signal is ignored.

8. Action-Fit Test

Is signal quality sufficient for the proposed action?

Failure signal: weak signal drives high-impact actuation.

9. Memory-Binding Test

Is signal strong enough to enter durable U7 memory?

Failure signal: provisional signal becomes permanent record.

10. Affected-Node Test

Does the signal include or preserve affected-node reality where relevant?

Failure signal: official signal omits the nodes most exposed to consequence.

19) Anti-Patterns

• Volume as quality
• Polish as reliability
• Metric as full signal
• Summary as source
• Repetition as confirmation
• Signal strength as truth
• Weak signal as irrelevant
• Official signal as default truth
• Affected-node signal as default bias
• High confidence from low context
• Classification before signal stabilization
• Memory binding from provisional signal
• Noise treated as evidence
• Silence treated as signal absence
• Freshness ignored
• Contradictions discarded
• Source incentives ignored
• Signal used beyond its quality range
• Emotional intensity as full causality
• Technical precision as full meaning

20) Spec Validation Check

• Is this truly a diagnostic, not an operator? Yes.
• Does it measure state, capacity, risk, or response rather than act directly? Yes.
• Does it map to S? Yes.
• Are U-layers specified? Yes.
• Are leading and lagging indicators separated? Yes.
• Are interpretation risks defined? Yes.
• Are operator sequencing implications clear? Yes.
• Are gate implications clear? Yes.
• Are scaling risks included? Yes.
• Are interaction implications included? Yes.
• Does it avoid new primitives? Yes.

Condensed Archive Summary

signal_quality is the diagnostic estimate of how clean, reliable, complete, traceable, current, context-rich, and usable a signal is for interpretation, classification, decision, repair, gate evaluation, or memory binding. It does not measure whether the signal is complete truth or whether action should occur automatically; it measures whether the signal is fit for the proposed use. Low signal_quality indicates risk of false classification, misrepair, misattribution, boundary error, proxy distortion, memory contamination, and high-confidence action from weak evidence. Under low signal_quality, Ψ observation, raw source preservation, Au provenance repair, Μ provisional interpretation, Θ certainty damping, Δ testing, confirmation, and reversibility should precede hard Γ, irreversible Π, punitive action, durable U7 binding, attribution closure, repair-complete claims, public certainty, or canonization.