Evidence row 548509

What was observed

{
  "count": 11,
  "tools": [
    {
      "description": "Given MTBF and MTTR (same time unit), return steady-state availability A = MTBF / (MTBF + MTTR). One-line closed-form, but worth a dedicated tool so LLMs don't fumble the identity (the most common mistake is conflating MTBF with MTTF and silently inflating availability by the MTTR). Use whenever a user supplies an MTBF/MTTR pair and asks for availability. ANTI-FABRICATION: exact closed-form. Quote verbatim.",
      "inputSchema": {
        "properties": {
          "mtbf": {
            "default": 1000,
            "description": "Mean Time Between Failures (repairable system). Same time unit as MTTR.",
            "minimum": 0,
            "type": "number"
          },
          "mttr": {
            "default": 10,
            "description": "Mean Time To Repair. Same time unit as MTBF.",
            "minimum": 0,
            "type": "number"
          }
        },
        "required": [
          "mtbf",
          "mttr"
        ],
        "type": "object"
      },
      "name": "compute_availability"
    },
    {
      "description": "Return the full worked-example doc for the bottling-line paired model — topology (5 machines: Filler/Capper/Labeler/Case Packer/Palletizer, 100 bottles/min, Weibull(30,1) TTF + Weibull(5,1) downtime at the Constraint-Level rollup), the two tracks (CT rollup vs LEDS-Level drill-down to 36 named failure modes), the 4 build sequences (BS1 → BS4), the file-shape mapping between ReliaSim outputs and ReliaStats modes, and a worked cross-MCP tool chain. Optional 'section' parameter narrows to one H2 section. ANTI-FABRICATION: content is sourced from docs/paired-model-bottling-line.md; every claim references the .aidos files or ChapterRegistry.fs in reliasim-site.",
      "inputSchema": {
        "properties": {
          "section": {
            "default": "",
            "description": "Optional H2 section name from docs/paired-model-bottling-line.md to narrow the response. Examples: 'Topology — the line itself', 'The two tracks — Constraint-Level vs LEDS-Level', 'The four build sequences (BS1 → BS4)', 'File-shape mapping — which ReliaStats mode consumes what', 'The cross-MCP workflow — worked example'. Omit to return the full doc.",
            "type": "string"
          }
        },
        "required": [],
        "type": "object"
      },
      "name": "describe_bottling_line"
    },
    {
      "description": "Return a textbook-tier explainer of advanced reliability patterns: censored data (right/left/interval — the rule not the exception), Maximum Likelihood Estimation, Goodness-of-Fit tests (Anderson-Darling favored over KS for tail-sensitive reliability work), the Confidence-Interval vs Prediction-Interval distinction that backs the Interrupt Validation scatter, accelerated life testing (Arrhenius / inverse power law / Coffin-Manson), and Bayesian reliability. No inputs. ANTI-FABRICATION: text is sourced from docs/reliability-theory.md.",
      "inputSchema": {
        "properties": {},
        "required": [],
        "type": "object"
      },
      "name": "explain_advanced_reliability_patterns"
    },
    {
      "description": "Return a textbook-tier distribution zoology for reliability work: why Weibull is the default, the shape-parameter β table mapping β-ranges to physical failure modes (β<1 infant mortality, β=1 random, β>1 wearout), when to reach for Exponential / Lognormal / Normal / Gamma, and practitioner heuristics for picking a distribution. No inputs. Use when a user asks 'which distribution should I fit' / 'what does Weibull β mean' / 'when to use Lognormal'. ANTI-FABRICATION: text is sourced from docs/reliability-theory.md. The β-as-failure-mode interpretation is ChiAha's practitioner framing — quote verbatim; do not paraphrase.",
      "inputSchema": {
        "properties": {},
        "required": [],
        "type": "object"
      },
      "name": "explain_distributions_for_reliability"
    },
    {
      "description": "Return the specific explainer for the ReliaStats Interrupt Validation scatter chart's red y=x / blue 95% Prediction Interval / teal 99% Confidence Interval reference lines. Use when a user asks 'what do the bands mean' / 'why is my point outside the blue line' / 'how do I read the validation scatter'. The bands are FIXED plotting conventions — they are NOT recomputed from the loaded data; this is anti-fab by design. Text sourced from docs/reliability-theory.md (the 'Confidence intervals vs prediction intervals' sub-section of Advanced Reliability Patterns).",
      "inputSchema": {
        "properties": {},
        "required": [],
        "type": "object"
      },
      "name": "explain_pi_vs_ci_for_validation"
    },
    {
      "description": "Return a textbook-tier explainer of reliability fundamentals: the four reliability functions R(t)/F(t)/f(t)/h(t), MTBF vs MTTF vs MTTR, the availability identity A = MTBF/(MTBF+MTTR), the bathtub curve, and series/parallel system reliability. No inputs. Use when a user asks 'what is reliability theory' / 'explain MTBF' / 'how does availability work' / 'what's a hazard rate'. ANTI-FABRICATION: text is sourced from docs/reliability-theory.md (the canonical ChiAha reliability primer). Quote sections verbatim; do not paraphrase reliability theory from training-data recall.",
      "inputSchema": {
        "properties": {},
        "required": [],
        "type": "object"
      },
      "name": "explain_reliability_basics"
    },
    {
      "description": "Given a Weibull shape parameter β (and optionally the characteristic-life parameter η), return a plain-language interpretation: which bathtub-curve regime β implies (infant mortality / random / wearout), what action that suggests (process-of-care / steady-state monitoring / maintenance scheduling), and — if η provided — closed-form MTTF and B-life numbers from the Weibull formulas. Pure-math + lookup, no engine call, fully deterministic. Use when a user reports a fitted β and wants to know what to DO with it. ANTI-FABRICATION: MTTF and B-life are exact closed-form values from the two-parameter Weibull (η · Γ(1+1/β) and η · (-ln(1-p))^(1/β)). Quote them verbatim.",
      "inputSchema": {
        "properties": {
          "beta": {
            "default": 2,
            "description": "Weibull shape parameter β (dimensionless). Typical reliability range 0.3 – 8.0.",
            "maximum": 20,
            "minimum": 0.01,
            "type": "number"
          },
          "eta": {
            "default": 1000,
            "description": "Optional Weibull characteristic-life parameter η, in the same time units you care about (e.g. hours). When provided, the response includes MTTF + B-life numbers.",
            "minimum": 0,
            "type": "number"
          }
        },
        "required": [
          "beta"
        ],
        "type": "object"
      },
      "name": "interpret_weibull_shape"
    },
    {
      "description": "Return the catalog of paired models — concrete real-world systems that live in two ChiAha sandboxes simultaneously, one for dynamics (DES via ReliaSim) and one for statistics (distribution fitting + validation via ReliaStats). Today: a single paired model — the bottling line. Returns canonical model IDs + cross-MCP routing metadata (which ReliaSim chapter, which ReliaSim MCP tools, which ReliaStats mode consumes which file shape). Use when a user asks about cross-MCP workflows, paired sandboxes, or the bottling-line example. ANTI-FABRICATION: this is a soft-reference catalog — to actually run a simulation, the LLM client calls ReliaSim's MCP tools directly.",
      "inputSchema": {
        "properties": {},
        "required": [],
        "type": "object"
      },
      "name": "list_paired_models"
    },
    {
      "description": "Given a free-text symptom description (e.g. 'manufacturing burn-in', 'bearing wearout under variable load', 'cosmic-ray bit flips'), return an ordered shortlist of distribution candidates with a one-line rationale per recommendation. Keyword-matched against a curated dictionary; ALWAYS treat output as a starting point for fitting work, not a fit. The actual fitting happens in the ReliaStats sandbox (protected/app.html). ANTI-FABRICATION: rationales are written ChiAha content; the algorithm is a deterministic substring match. Quote verbatim.",
      "inputSchema": {
        "properties": {
          "symptoms": {
            "default": "bearing wearout",
            "description": "Free-text description of the failure data or context — e.g. 'manufacturing burn-in', 'bearing wearout', 'cosmic-ray bit flips', 'multi-stage degradation'. Substring-matched against a keyword dictionary; returns an ordered shortlist with rationale.",
            "type": "string"
          }
        },
        "required": [
          "symptoms"
        ],
        "type": "object"
      },
      "name": "recommend_distribution"
    },
    {
      "description": "Given per-component reliabilities and a structure ('series' or 'parallel'), return the system reliability. Series = product (all must work). Parallel = 1 − product(1−Rᵢ) (at least one works). Useful for back-of-envelope RBD calcs before reaching for full RBD tooling. For mixed-structure systems (series with parallel sub-blocks), call this tool repeatedly on the sub-blocks. ANTI-FABRICATION: exact closed-form. Quote verbatim.",
      "inputSchema": {
        "properties": {
          "components": {
            "default": [
              0.95,
              0.95,
              0.95
            ],
            "description": "Per-component reliabilities in [0, 1]. Order doesn't matter.",
            "items": {
              "maximum": 1,
              "minimum": 0,
              "type": "number"
            },
            "type": "array"
          },
          "structure": {
            "default": "series",
            "description": "RBD structure: 'series' (all must work) or 'parallel' (at least one works).",
            "enum": [
              "series",
              "parallel"
            ],
            "type": "string"
          }
        },
        "required": [
          "components",
          "structure"
        ],
        "type": "object"
      },
      "name": "system_reliability"
    },
    {
      "description": "Given Weibull two-parameter (β, η), return all the closed-form summary statistics: MTTF (η·Γ(1+1/β)), B10 / B50 / B90 life, characteristic life (just η, surfaced explicitly), and — if evaluateAtT supplied — R(t), F(t), and hazard h(t) at that time. Pure-math, fully deterministic. Use when the user has a fit and wants the numbers downstream tools normally compute (don't recompute these from training-data recall — call this tool). ANTI-FABRICATION: every number is an exact closed-form value. Quote verbatim.",
      "inputSchema": {
        "properties": {
          "beta": {
            "default": 2,
            "description": "Weibull shape parameter β (dimensionless).",
            "maximum": 20,
            "minimum": 0.01,
            "type": "number"
          },
          "eta": {
            "default": 1000,
            "description": "Weibull characteristic life η, in your chosen time unit.",
            "minimum": 0,
            "type": "number"
          },
          "evaluateAtT": {
            "default": 500,
            "description": "Optional time t (same unit as η) at which to also return reliability R(t), failure F(t), and hazard h(t).",
            "minimum": 0,
            "type": "number"
          }
        },
        "required": [
          "beta",
          "eta"
        ],
        "type": "object"
      },
      "name": "weibull_summary"
    }
  ]
}

What the leaf hashes

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Proof

Fold these into the leaf in order, hashing each sorted pair with keccak256, to reach the root of 2026-10-27 (273461 rows).

1. 0x3c64a4ec8640ea8f89cee812185a2f2d45d3bea413801d619de09c0dade53057
2. 0x602d491d26ca432e278677f9d0c6d58d7926a95e03eb0dfcf1c91c54f849e043
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5. 0x81227877a276c5708a386deb82860de204a3525c09525494a6a796c8a7834fab
6. 0x1f8735d4f2f99193efb2b7b2f0f6ada160d5e0b90e9264c5f96064d1d73c263d
7. 0xaa9eaf853fd797abbe3f0e1c12386dfb2c7fa2b3b8e9636936d0ab14b3990ef4
8. 0x8743f0c488065505f7ebc3f79a3967341e0a463a1c03fc359ee3ebbdf1f431f1
9. 0x534d82dc201dc1c3d8f412528c3a713a7c5d9d5bc25a0fdb3f893b3ebfe37dbe
10. 0x20ce165a0d8336fea141e0c371351a7bbb12f065d8ef43ab00c2247475a52c3d
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19. 0x9fc354c0ea3efc3f1b6548bb77ea902a50f2c9647647ea61592efce2bd795699

Day root

0xdd77e5e99da32c3f154180d63e3c3e6fe11e896a2c6896412ef6b5c4bcf7e795

Chained

0x23bb4dfd09f55c15ac3962f53578e02c6114d5bfd9bb451cf8794fb6e2817ef4

Published

On Arc Testnet, block 69414147: 0xb2ebd83aafeb765a0ce66e76aefb133ed06606c98038fd56191e2bcd1dcb77b4