@prefix this: . @prefix sub: . @prefix np: . @prefix rdf: . @prefix prov: . @prefix npx: . @prefix dc: . @prefix xsd: . sub:Head { this: a np:Nanopublication; np:hasAssertion sub:assertion; np:hasProvenance sub:provenance; np:hasPublicationInfo sub:pubinfo . } sub:assertion { a ; dc:description "analysis.py imports run() from the levin-2023-competency-04 re-implementation and re-runs hardwired and competency 20, 100 and 400 populations, 100 repeats of 250 generations each, from a new master seed. It compares best genotypic fitness at generation 250 with hardwired by one-sided Welch t-tests, measures the change in the mean genotypic curve between generations 126-150 and 226-250, the across-repeat SD of best genotypic fitness over generations 51-250 per level, and best phenotypic fitness at generation 250."; "Measured: 0.583921 mean best genotypic fitness at generation 250, competency 400 (hardwired: see extra) (n = 100, Monte Carlo; fixed seed, deterministic for a given numpy version). Interpretation: Simulation-result claim, tested by replication. 'Genotypic fitness of the best individual' is taken, as in the authors' Exp1 code, as the maximum pre-development fitness in the population each generation. 'Plateaued' is operationalised as < 0.03 change in the repeat-mean curve between generations 126-150 and 226-250; 'wider variance' as the across-repeat SD of best genotypic fitness averaged over generations 51-250; 'high phenotypic fitness' as mean best phenotypic fitness >= 0.95 at generation 250. 'Reduced selective pressure on structural genes' is the authors' interpretation of the lower plateau and is not measured separately."; "Claim test of levin-2023-competency-05: supports, confidence 0.9"; ; prov:wasAttributedTo ; "Runs of the model exactly as specified in the paper and its supplement (50-cell arrays, 100 embryos, top-10% phenotypic selection, crossover, mutation probability 0.6, competency 20/100/400); unspecified details from the authors' public code; fresh random seeds independent of the companion test levin-2023-competency-04."; "claim-test/0.1.0"; "computational"; "Competent genotypic fitness reaching or exceeding hardwired, continuing to climb, or showing less spread at higher competency."; "All pre-registered criteria hold: genotypic fitness at generation 250 is 0.973, 0.872 and 0.584 at competency 20, 100 and 400 vs 1.000 hardwired (all p < 1e-80); the curves change by < 0.003 between the two windows; across-repeat SD rises 0.0040 < 0.0094 < 0.0211; phenotypic fitness is 1.0 at every level.", "How long genotypic fitness tracks hardwired before plateauing depends strongly on competency: the competent mean first falls 0.02 below hardwired at generation 85, 33 and 6 for competency 20, 100 and 400. 'Rose briefly' fits competency 400 best; at competency 20 the genotypic curve follows hardwired for most of the climb.", "The companion test levin-2023-competency-04 had already reported generation-250 genotypic means (0.97, 0.87, 0.59) before this test was pre-registered, so the direction of criterion A was known; this run uses independent seeds.", "The repository was restructured while this test ran (claim-tests/ moved to test-data/claim-tests/, books/ to claim-sources/); the pre-registered planned_datasets keep the old paths.", "This is a replication of a simulation, not an empirical test: it shows what follows from the stated model, not what real cellular competency does."; "Competent populations' best genotypic fitness rises for a few generations, then flattens below the hardwired curve, with across-repeat spread increasing with competency level and phenotypic fitness near maximal."; "Pre-registered: refutes if any competent level's mean best genotypic fitness at generation 250 is >= hardwired, or the generation-51-250 SD at competency 400 is below that at competency 20, or any competent level's genotypic mean curve still rises by more than 0.1 between the 126-150 and 226-250 windows. Any other failure of (A)-(D) -> mixed."; "0.9"^^xsd:double; , , ; "supports"; "In a re-run of the Shreesha & Levin (2023) Experiment 1 model (fixed competency 20/100/400 vs hardwired, 100 repeats x 250 generations), the best genotypic fitness of each competent population stops rising and stays below the hardwired population's, its spread across repeats grows with competency, while phenotypic fitness stays high."; "Mean curves over 100 repeats; one-sided Welch t-test at generation 250 (alpha 0.001); plateau as window-mean difference < 0.03; spread as mean across-repeat SD over generations 51-250 compared across levels."; "Pre-registered, with 'best genotypic fitness' = the maximum genotypic (pre-development) fitness in the population each generation, as in the authors' code: (A) at generation 250 each competent population's mean best genotypic fitness is below hardwired, one-sided Welch t-test p < 0.001; (B) plateau: for each competency level, |mean of the mean curve over generations 226-250 minus mean over generations 126-150| < 0.03; (C) the across-repeat standard deviation of best genotypic fitness, averaged over generations 51-250, is strictly ordered competency 20 < 100 < 400; (D) mean best phenotypic fitness at generation 250 >= 0.95 for every competency level. All four -> supports."; "false"^^xsd:boolean . . a ; dc:description "per-generation best genotypic and phenotypic fitness; mean and SD curves in derived/genotypic_curves.json"; dc:license ""; "Dataset: Re-run of the Shreesha & Levin (2023) Experiment 1 re-implementation, 100 repeats x 250 generations x 4 populations (generated by analysis.py)" . a ; dc:description "model specification (Sections 2-3, Discussion); full text vendored in claim-sources/levin-papers/inputs/2023-competency.txt"; dc:license "CC BY 4.0"; "Dataset: Shreesha L, Levin M (2023) Cellular Competency during Development Alters Evolutionary Dynamics in an Artificial Embryogeny Model. Entropy 25(1):131"; prov:wasDerivedFrom . a ; dc:description "definition of the tracked 'best genotypic fitness' (population maximum) and unspecified model details; not imported"; dc:license ""; "Dataset: Authors' simulation code for Shreesha & Levin (2023), CellularCompetency"; prov:atLocation , , ; prov:wasDerivedFrom ; "sha256:55ad59da900e896f2b59b153c8dcdfe9371d0c3f01a8fc5ba3638a430badc1d6", "sha256:8357fe689efe9ab76efd8c5f39bbf474a5b15ff4381be856b17a6dbc7c080c46", "sha256:907b43e5e5a37f3680af1064302ca77e1dde91f882150881c9c0f0486e2f7846" . } sub:provenance { a prov:SoftwareAgent; "delphisci claim tester (claude-opus-5.5; cursor local session)"; prov:actedOnBehalfOf ; . a ; dc:identifier "claude-opus-5.5" . sub:assertion prov:wasAttributedTo ; prov:wasDerivedFrom ; prov:wasGeneratedBy sub:test . sub:plan prov:hadPlan . sub:test a prov:Activity; prov:qualifiedAssociation sub:plan; prov:wasAssociatedWith . } sub:pubinfo { this: dc:created "2026-11-08T16:24:31.879Z"^^xsd:dateTime; dc:creator ; dc:license ; npx:hasNanopubType ; npx:introduces ; "Claim test of levin-2023-competency-05: supports, confidence 0.9"; . sub:sig npx:hasAlgorithm "RSA"; npx:hasPublicKey "MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEAyFHtPB6p0mCjDozxlOY9CmO3nBm7Te3jFbXyLmM+G1tGe47Da0jYIbhkl9Oo1ZjxmCa3LCwlEqK8gj6hEIcL4ahW30JXh9HycfKVk1bugkzMEV15tJdTzAU3sGMFnbNsn43xY2LFLy45tApkRm5byEiM7oRRrooIdwoQKnP4Wzq/bfAoJ9qvW+MGkelyOTd1jbSt+KfmT7xKmDfWzAHddUlWwMX2QJOLxDaffOteTop6ZRgPiNmPJKA/S62Gac9XtSYT3oDkZ6/nr5cxxNLpOyxTf5FPISiCe11xYO/unYjY+mwnOfA8yhJIyx1l0O9I0vpOpn+E5TvyHJV+oSTrvwIDAQAB"; npx:hasSignature "VPAGht8uAfIiW376guWK6bI/CtZdz9gHNSE1zslKnVwdP3X2XWLyrvgZIVDBFO0XuxVNgf/m6JQEOvyg43zi8WJAy9IWhSTF7dUFMkWhT+HNQtoCWeFE4i3qgzco8hAtQ0oiSbWvVbkzJ6aDP2AfGhR7Lb4dyUBD493YtgLYSy/uaGSlfXlnCuT2IjjouViy4/Y8UV4fwVAinfSooWXvssLvCwtIxQTAsQJw+FYSS+IkwnrIZ1hJkfy7MKRXLls2Ttk67Keju91fEmhYwX7RVW8dstRpJ4sJhsbtWh0yeGYPJD9+DuB3Zcwjb5QG8a7pvsy3snBZG+G3ROLRmqSlnQ=="; npx:hasSignatureTarget this:; npx:signedBy . }