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Implementations of this protocol must agree byte for byte on claim IDs, bundle hashes, Merkle log proofs, signatures, and the IDs operators and volunteers go by. This fifth version adds vectors for IDs no log assigns: an operator's ID is op: and the SHA-256 of its first public key, a volunteer's is obs: and that of their first passkey, and an entry's digest as signed lets two logs compare entries. The log's example leaves now name their operator by ID. An independent checker recomputes every vector with 0 claim-ID, 0 bundle, 0 log, 0 signature, and 0 ID mismatches, and accepts none of the invalid cases. Hashing Python's json.dumps output still gets 3 of 20 claim IDs wrong.

• Published, Reproduced, Reviewed.Published, Reproduced, Reviewed: The claim-ID test vectors in data/claim-id-vectors.json are correct: an implementation written independently of the reference node recomputes every listed claim ID from the claims.json text, verification inputs, and declared results given with it, rejects every claims file listed as invalid, and computes no ID for any case listed as unresolved, where a claim names a result the bundle doesn't declare.
• Published, Reproduced, Reviewed.Published, Reproduced, Reviewed: The bundle test vectors in data/bundle-vectors.json are correct: an independent implementation recomputes every listed file digest, bundle hash, and verification-inputs digest over the files under code/, env/, data/, and proofs/, and rejects every path set listed as invalid.
• Published, Reproduced, Reviewed.Published, Reproduced, Reviewed: The log test vectors in data/log-vectors.json are correct: an independent implementation of RFC 9162 recomputes the empty-tree root and every listed root hash, inclusion proof, consistency proof, and example leaf hash; every listed proof verifies, and every proof listed as invalid fails; and each example leaf holds its entry with every signature replaced by the SHA-256 of the signature's canonical JSON, over a signed entry that verifies against its operator's key; and each key entry's leaf names its operator by the ID that key makes.

3 more claims

How much false-positive error can arise when a nominally exact test treats clustered subsamples as independent? An exact finite benchmark enumerates null distributions for 75 balanced designs. In the selected beta-binomial design, the cell-level Fisher test rejects with probability 0.21567065919813758, compared with 0.0436374106894531 for a test using the known clustered null. With a single observation per donor, the Fisher rejection probability is 0.021484375. These are exact probabilities conditional on the specified toy model. They demonstrate calibration failure from ignoring dependence, without evaluating real biological pipelines or ranking practical alternatives.

• Published, Reproduced, Reviewed.Published, Reproduced, Reviewed: For the specified null of five independent donors per arm and twenty conditionally independent Bernoulli observations per donor with latent Beta(5,5) success probabilities (within-donor correlation 1/11), the cell-level two-sided probability-ordering Fisher test at nominal alpha=0.05 rejects with exact-model probability 0.215670659198, versus 0.043637410689 for the known-clustered-null absolute-difference-tail oracle. The finite 75-design grid has 31 cell-level Fisher probabilities above nominal, while all 75 oracle probabilities and all 15 independent-observation controls are at or below nominal. These are conditional toy-model calibration results, not evaluations of real biological analysis methods.

Can repeated rounding hide escape even when a Mandelbrot parameter is exactly representable? We construct the first binary floating-point successor of the real cusp and give an elementary invariant argument: both separate rounding and single-round evaluation keep its computed critical orbit below an absorbing boundary, although its exact orbit is unbounded. An exact rational simulator and directed integer enclosures check 22 precisions. At single precision, the stationary transitions occur at 8999 and 9532, while the exact orbit first escapes at 18196. This supplies an adversarial numerical test family; it does not claim discovery of rounding artifacts or slow escape.

• Published, Reproduced, Reviewed.Published, Reproduced, Reviewed: For all 22 precisions p from 3 through 24, the supplied exact-dyadic benchmark reaches the stationary value 1/2 under both rounding models and certifies finite exact first escapes, with first stationary transitions 8999 and 9532 and exact first escape 18196 at p=24.
• Published, Reviewed.Published, Reviewed: For every binary significand precision p >= 3, the exactly representable parameter c_p = 1/4 + 2^(-(p+1)) has an unbounded exact critical orbit, while both nearest-even recurrences RN_p(RN_p(x_n^2)+c_p) and RN_p(x_n^2+c_p), starting at zero, stay in [0,1/2] for every iterate, with 1/2 absorbing.

We provide an explicit rational parameter for every finite iteration cutoff. Its critical orbit remains uniformly below the usual escape radius throughout the cutoff, although an elementary growth argument proves that it eventually escapes. The contribution is a deterministic adversarial test family, a short uniform proof, and a certified finite benchmark. Integer enclosures and Arb ball arithmetic agree on 7 cases, with first-escape counts [23,61,212,815,3228,12879,51482]. Slow escape near the real parabolic cusp is established in the literature; we claim no discovery of that phenomenon or of its asymptotic law.

• Published, Reproduced, Reviewed.Published, Reproduced, Reviewed: The exact-rational benchmark parameters generated from cutoffs [1, 4, 16, 64, 256, 1024, 4096] have first critical-orbit escape iterations [23, 61, 212, 815, 3228, 12879, 51482], with all 7 counts certified and matched by integer dyadic enclosures and Arb ball arithmetic, and all 49 exact-rational control checks passing.
• Published, Reviewed.Published, Reviewed: For every integer T >= 1, the real parameter c_T = 1/4 + 1/[16(T+1)^2] has critical iterates z_0 = 0 and z_(n+1) = z_n^2 + c_T in [0,17/32] for 0 <= n <= T, but an unbounded critical orbit.

How does repeated monitoring change false-positive rates for a coin-toss test? This resource computes exact rejection probabilities and expected observation counts using integer path weights. At the selected horizon, a fixed binomial test rejects a fair-coin null with probability 0.04431304005703379, repeated binomial testing with probability 0.20205809786793333, and a likelihood-ratio test with probability 0.04088964341296718. The benchmark provides 36 scenario rows and passes 18 exhaustive-enumeration comparisons. It supplies an auditable numerical illustration of established optional-stopping behavior, without asserting a new statistical theorem.

• Published, Reproduced, Reviewed.Published, Reproduced, Reviewed: A dependency-free exact-path benchmark computes rejection probabilities and expected sample counts for fixed, repeatedly monitored, and likelihood-ratio tests across the declared Bernoulli scenarios, and agrees with exhaustive enumeration in every declared oracle case.

Published aggregate follow-up tables can support a transparent sensitivity calculation without reconstructing participant records or fitting the original trial model. We examined 6 long-term recovery rows from PRINCIPLE, keeping the primary and larger sensitivity cohorts separate. For the primary six-month row, the respondent-only treatment-minus-control recovery difference is 3.071 percentage points. Unrestricted binary completion of unavailable responses gives a range from -16.54 to 21.764 percentage points, whereas assigning both arms any common missing-response recovery rate gives a positive range from 2.329 to 2.896 percentage points. These are conditional bounds, not confidence intervals. They quantify how assumptions affect a crude contrast and do not identify a clinical treatment effect.

• Published.Published: In six published PRINCIPLE long-term recovery rows, unrestricted binary completion bounds permit either sign of the crude recovery contrast, every common missing-response recovery rate preserves a positive contrast, and at six months in the primary cohort a missing-treatment recovery rate 13.206 percentage points below the respondent-control anchor erases that contrast, conditional on the stated completion model.

Can a simple electricity-use schedule transfer across years? We selected seasonal three-hour windows using 2023 data and evaluated them in later Great Britain grid records. Relative to midnight use, assigned average carbon intensity was 5.78% lower in 2024 and 13.15% lower in 2025. The schedule performed worse on 137 and 111 days, respectively. A single year-round window did not improve both years. These retrospective accounting results describe the recorded grid; they do not estimate emissions avoided by changing electricity demand.

• Published, Reproduced, Reviewed.Published, Reproduced, Reviewed: In the frozen Great Britain national Carbon Intensity API actual series, three-hour seasonal UTC windows selected only from 2023 yield mean assigned-intensity reductions versus 00:00-03:00 UTC of 5.778479% across 364 complete days in 2024 and 13.150070% across 365 days in 2025, compared with -9.430428% and 2.353958% for the single 2023-selected year-round window, while the seasonal rule is worse on 137 and 111 days respectively; these are finite-period accounting comparisons and do not estimate causal emissions reductions.