Evidence row 266817

What was observed

{
  "count": 6,
  "tools": [
    {
      "description": "Discrete Bayesian update: given competing hypotheses each with a prior and the likelihood of the observed evidence, return normalised posteriors. Priors are renormalised to sum to 1.",
      "inputSchema": {
        "properties": {
          "hypotheses": {
            "items": {
              "properties": {
                "likelihood": {
                  "type": "number"
                },
                "name": {
                  "type": "string"
                },
                "prior": {
                  "type": "number"
                }
              },
              "required": [
                "prior",
                "likelihood"
              ],
              "type": "object"
            },
            "type": "array"
          }
        },
        "required": [
          "hypotheses"
        ],
        "type": "object"
      },
      "name": "bayes_update"
    },
    {
      "description": "Confidence interval for a mean (t-based; from data, or n/mean/sd) or a proportion (Wilson; successes/n). kind = mean | proportion; confidence default 0.95.",
      "inputSchema": {
        "properties": {
          "confidence": {
            "type": "number"
          },
          "data": {
            "items": {
              "type": "number"
            },
            "type": "array"
          },
          "kind": {
            "enum": [
              "mean",
              "proportion"
            ],
            "type": "string"
          },
          "mean": {
            "type": "number"
          },
          "n": {
            "type": "number"
          },
          "sd": {
            "type": "number"
          },
          "successes": {
            "type": "number"
          }
        },
        "type": "object"
      },
      "name": "confidence_interval"
    },
    {
      "description": "Summary statistics for a numeric array: mean, median, sd, variance, quartiles, IQR, skewness, min/max.",
      "inputSchema": {
        "properties": {
          "data": {
            "items": {
              "type": "number"
            },
            "type": "array"
          }
        },
        "required": [
          "data"
        ],
        "type": "object"
      },
      "name": "descriptive_stats"
    },
    {
      "description": "Evaluate a probability distribution (normal, t, chi2, binomial, poisson): pdf/pmf and cdf at a value, and/or the quantile at a probability, plus mean & variance. Params per dist: normal {mean,sd}, t {df}, chi2 {df}, binomial {n,p}, poisson {lambda}.",
      "inputSchema": {
        "properties": {
          "at": {
            "description": "value to evaluate pdf/pmf and cdf at",
            "type": "number"
          },
          "dist": {
            "enum": [
              "normal",
              "t",
              "chi2",
              "binomial",
              "poisson"
            ],
            "type": "string"
          },
          "p": {
            "description": "probability to get the quantile for (0-1)",
            "type": "number"
          },
          "params": {
            "type": "object"
          }
        },
        "required": [
          "dist"
        ],
        "type": "object"
      },
      "name": "distribution"
    },
    {
      "description": "Run a significance test and get the statistic, p-value, and a plain-language interpretation with assumptions. test = one-sample-t {data, mu0}, two-sample-t {data1, data2}, one-proportion-z {successes, n, p0}, two-proportion-z {successes1,n1,successes2,n2}, chi2-gof {observed, expected?}, chi2-independence {table}. Optional tail: two-sided (default) | greater | less; alpha default 0.05.",
      "inputSchema": {
        "properties": {
          "alpha": {
            "type": "number"
          },
          "data": {
            "items": {
              "type": "number"
            },
            "type": "array"
          },
          "data1": {
            "items": {
              "type": "number"
            },
            "type": "array"
          },
          "data2": {
            "items": {
              "type": "number"
            },
            "type": "array"
          },
          "expected": {
            "items": {
              "type": "number"
            },
            "type": "array"
          },
          "mu0": {
            "type": "number"
          },
          "n": {
            "type": "number"
          },
          "n1": {
            "type": "number"
          },
          "n2": {
            "type": "number"
          },
          "observed": {
            "items": {
              "type": "number"
            },
            "type": "array"
          },
          "p0": {
            "type": "number"
          },
          "successes": {
            "type": "number"
          },
          "successes1": {
            "type": "number"
          },
          "successes2": {
            "type": "number"
          },
          "table": {
            "type": "array"
          },
          "tail": {
            "type": "string"
          },
          "test": {
            "type": "string"
          }
        },
        "required": [
          "test"
        ],
        "type": "object"
      },
      "name": "hypothesis_test"
    },
    {
      "description": "Simple linear regression of y on x: slope, intercept, r, r², slope std error and p-value, equation.",
      "inputSchema": {
        "properties": {
          "x": {
            "items": {
              "type": "number"
            },
            "type": "array"
          },
          "y": {
            "items": {
              "type": "number"
            },
            "type": "array"
          }
        },
        "required": [
          "x",
          "y"
        ],
        "type": "object"
      },
      "name": "linear_regression"
    }
  ]
}

What the leaf hashes

{"body":"6ca8fcc507e2138ea0b8aaecb7b87e11be5c54d6f9fea48e96e690c755898f4f","id":266817,"kind":"mcp.tools","method":"probe.mcp/1","observed_us":1793035548510235,"prev":null,"subject":"ed4c18ff-66d4-4d8d-9e08-1c3b60a230b6","subject_kind":"endpoint"}

Proof

Fold these into the leaf in order, hashing each sorted pair with keccak256, to reach the root of 2026-10-26 (296695 rows).

1. 0x909c648e87ad80a3a0c00862bfd111dd7affda2a177f3ddc319a937c2c7c3b68
2. 0x28af8aee38001b5feac02a08acbd01b978b19d635579e895d7fc3e09d9313075
3. 0x47a57764a90c3e056f5a14826b409a2609cbda25a8058137e74deceb09d46469
4. 0x907a810a398deaf13adf938e66160593d50e91f091d10c26c0bc18031b1a8481
5. 0xb0ba62e5d2f544d29f78c6b6e66eeb6437f2820137ae349e00cf65c31c8cdb87
6. 0x1dd570768c252d5863b050fd08c3e46c742fb8df9588fe703b83571d3a6af455
7. 0xb7dcbbc3d153fa7c7e7dc29b6d40dc0d19928fbc29dc4888e9798849cd0836e9
8. 0x1bbe99110459f77367223a690206bf73faf8261d1e561757ef785e6fb6430171
9. 0x34218488b946088cefda1e91ecf658e451b5fa9de339f66946fe548fbb5cf034
10. 0xaceb83c372739bd98e77173713a458afd2092803e86ac0b8b2afe3eb94ca90c0
11. 0xc41c5ee73cf254bf3fe8e9a8475b0931c3223f7331f30010bc3a5e2a753329d6
12. 0x4634efb8094d1dd2874013b832ad8653e49400f57bf98efdcc5f925ed08d0f5b
13. 0xc0cb184c9bdf71abcf18fda97bd59289153d120dbf8fee9f517b63bf303deb95
14. 0xf0edabbb9ba0d643e5505d19d07637eb6f6aed0a1c7356af43ae7d84ae9ef0c5
15. 0xf0bd63870f0bb378fa300bd33563050dcb7b3e02b44eb3ee550c1c534ec4b246
16. 0x1ac20d30773e1f4f2f811540e9a99b5cca9177c9e2e029acc0d80cf3f1d11f0f
17. 0xd21942ae3214eff88e08ab04884fd4b11958b51af9a0741b7668e2d07a2c56c9
18. 0x9a9024edb5069d88a324facb310a5ce8e495214bbaa968f077bede437390194f
19. 0x9b9ce5b74b9bab5121612ef6b64920a8d453cbf311096a423eef9702747e868a

Day root

0xf43e8dcc3d451a4788b26be904c55e8c4c0fa75c9666796c51db38d37e2c0df8

Chained

0x702d5ac508f08b0d1accf36af25822d8e5660367494edd17a642fb61e158a57a

Published

On Arc Testnet, block 69251483: 0x495162f8936e19f0039918feebe6c307f327cd6bf8c37219620b1970041fa9ae