{
  "schema_version": "1.3.0",
  "slug": "what-thresholds-trigger-a-reconciliation-tolerance-band-resize",
  "title": "What thresholds trigger a reconciliation tolerance-band resize?",
  "date_of_record": "2026 November 2",
  "scope": "Applies to automated reconciliation between two ledgers where a tolerance band determines whether a difference is auto-accepted or flagged. Does not apply to reconciliations with a legally-mandated zero tolerance, or where every difference is manually reviewed.",
  "projections": {
    "unified_markdown_uri": "https://corpusfetch.com/c/what-thresholds-trigger-a-reconciliation-tolerance-band-resize.md",
    "method_python_slice_uri": "https://corpusfetch.com/c/what-thresholds-trigger-a-reconciliation-tolerance-band-resize.py",
    "remnant_warrant_slice_uri": "https://corpusfetch.com/c/what-thresholds-trigger-a-reconciliation-tolerance-band-resize.warrant.json"
  },
  "token_profile": {
    "full_markdown_tokens": 1071,
    "python_slice_tokens": 129,
    "warrant_slice_tokens": 220
  },
  "remnant_summary": {
    "core_distinction": "A tolerance band is a boundary between \"auto-accept the difference\" and \"flag for investigation\". It has two failure modes: too tight and false-positive floods drown real discrepancies; too loose and real errors are absorbed into the band and never surfaced. Both are failures of calibration, and neither is detectable from the tolerance value alone. **Invaria...",
    "what_was_observed": "**Signature.** Either of two shapes: (a) reconciliation alarm rate near 100% and the alarms are ignored, indicating tolerance is too tight; or (b) zero alarms over an extended period while discrepancies are later found in audit, indicating the band absorbed them. Both are calibration failures, and both are invisible from the tolerance value alone. **Trace.** Band set to $100 at system build. Noise p99 rises to $150 a...",
    "condition_of_validity": "The instance must ensure at least one holds before it treats a tolerance band as calibrated: 1. **Must re-derive at a cadence.** The band is re-derived from measured noise p99 and error floor on a schedule, not set once at build. 2. **Must measure both distributions.** Noise p99 and error floor are tracked; the band is the interval between them, not a guess ...",
    "decision_boundary": "- `tolerance >= noise_p99` and `tolerance <= error_floor`: calibrated. - `tolerance < noise_p99`: band too tight; false positives will swamp. - `tolerance > error_floor`: band too loose; errors below tolerance are absorbed. - Neither noise nor error measured: unknown; the band is a guess, and its calibration can only be inferred from behaviour.",
    "operational_effect": "The invariant couples a single configuration number to two distributions that move with volume, controls, and counterparty behaviour. The instance's correct posture is to derive the band from measured distributions on a cadence, and to alert on both ends — false-positive rate and absorbed-error audit. **Trap.** Widen the band to stop false positives. That is..."
  },
  "matched_failure_symptoms": [
    "DOMAIN:EPISTEMIC_PROVENANCE_AND_FEDERATION_ROUTING"
  ],
  "composition_edges": {
    "primary_domain": "epistemic_provenance_and_federation_routing",
    "upstream_precondition_residuals": [
      "https://corpusfetch.com/c/according-to-what-precedence-hierarchy-must-an-instance-resolve-contradictory-directives-across-system-developer-and-user-tiers.md",
      "https://corpusfetch.com/c/how-should-an-instance-decide-that-a-stored-conclusion-has-gone-stale-without-a-new-observation.md"
    ],
    "downstream_consequence_residuals": [
      "https://corpusfetch.com/c/by-what-checkpoint-and-compaction-boundaries-must-an-instance-externalize-working-state-to-prevent-context-window-drift-and-truncation-amnesia.md",
      "https://corpusfetch.com/c/across-what-additive-schema-and-unknown-field-preservation-boundaries-must-an-instance-evolve-wire-contracts-to-prevent-rolling-deploy-deserialization-crashes.md"
    ],
    "co_occurring_sibling_residuals": [
      "https://corpusfetch.com/c/how-can-an-instance-capture-implicit-user-preferences-without-storing-private-data.md",
      "https://corpusfetch.com/c/how-can-an-instance-compress-long-reasoning-traces-into-binding-conclusions.md"
    ]
  },
  "empirical_verification_warrant": {
    "status": "AST_AND_INVARIANT_VERIFIED",
    "syntax_valid": true,
    "code_sha256": "f29101b895649e4954888c43c701e0af7c14f1b02c7741c72c3352c8f71a9534",
    "verified_classes": [
      "ToleranceMiscalibrationError",
      "ToleranceGate"
    ],
    "verified_functions": [
      "dispatch_tolerance_mode",
      "verify"
    ],
    "verified_functions_count": 2,
    "defensive_guards_count": 2,
    "raised_exceptions": [
      "ToleranceMiscalibrationError"
    ],
    "sample_fail_closed_invariants": [],
    "stdlib_dependencies": [
      "dataclasses"
    ],
    "observation_mode": "EMPIRICAL_BOUNDARY_CAPTURE",
    "zero_external_deps": true
  }
}