https://w3id.org/np/RAKQKqOtRZGsCrzYXVjxPWokptmEjzjuegaVQ6Mx5ekRo/Head https://w3id.org/np/RAKQKqOtRZGsCrzYXVjxPWokptmEjzjuegaVQ6Mx5ekRo http://www.nanopub.org/nschema#hasAssertion https://w3id.org/np/RAKQKqOtRZGsCrzYXVjxPWokptmEjzjuegaVQ6Mx5ekRo/assertion https://w3id.org/np/RAKQKqOtRZGsCrzYXVjxPWokptmEjzjuegaVQ6Mx5ekRo http://www.nanopub.org/nschema#hasProvenance https://w3id.org/np/RAKQKqOtRZGsCrzYXVjxPWokptmEjzjuegaVQ6Mx5ekRo/provenance https://w3id.org/np/RAKQKqOtRZGsCrzYXVjxPWokptmEjzjuegaVQ6Mx5ekRo http://www.nanopub.org/nschema#hasPublicationInfo https://w3id.org/np/RAKQKqOtRZGsCrzYXVjxPWokptmEjzjuegaVQ6Mx5ekRo/pubinfo https://w3id.org/np/RAKQKqOtRZGsCrzYXVjxPWokptmEjzjuegaVQ6Mx5ekRo http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.nanopub.org/nschema#Nanopublication https://w3id.org/np/RAKQKqOtRZGsCrzYXVjxPWokptmEjzjuegaVQ6Mx5ekRo/assertion https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-10--claim-test http://purl.org/dc/terms/description The repository contains MEMORY_EVALUATOR_ODE_SLURM1_Noise.m and no output matrix of that noise assay. The ODE right-hand sides are absent, so the noise comparison was not re-run. https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-10--claim-test http://www.w3.org/1999/02/22-rdf-syntax-ns#type https://w3id.org/delphi/ontology/Assessment https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-10--claim-test http://www.w3.org/2000/01/rdf-schema#comment Measured: null null. Interpretation: Noise here is noise added inside the ODE simulations. The result does not show that biological noise helps memory in cells. data_kind will be computational. https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-10--claim-test http://www.w3.org/2000/01/rdf-schema#label Claim test of levin-2022-grn-learning-10: inconclusive, confidence 0.35 https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-10--claim-test http://www.w3.org/2000/01/rdf-schema#seeAlso https://github.com/fractastical/delphisci/blob/bf2af6ee0a84e23ffa6414ad7a0a50a8c276506e/test-data/claim-tests/levin-papers/levin-2022-grn-learning-10/result.json https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-10--claim-test http://www.w3.org/ns/prov#wasAttributedTo https://w3id.org/delphi/resource/agent/delphisci-claim-tester-grok-4-7 https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-10--claim-test https://w3id.org/delphi/ontology/admissibleDatasets The same published ODE models used for the noiseless memory assay, re-integrated with a stated stochastic perturbation. A typed copy of the paper's 83% and 20% is not admissible. https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-10--claim-test https://w3id.org/delphi/ontology/assessmentType claim-test/0.1.0 https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-10--claim-test https://w3id.org/delphi/ontology/dataKind none https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-10--claim-test https://w3id.org/delphi/ontology/disconfirmingObservation Fewer than 83% of those models keep a memory, or mean memory amount falls when noise is added. https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-10--claim-test https://w3id.org/delphi/ontology/evidenceAgainst The noise script is present and its output matrix is not. Intact-fraction and the 20% increase were not measured. https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-10--claim-test https://w3id.org/delphi/ontology/expectedObservation At least 83% of models that had a memory without noise still have one with noise, and mean memory amount rises by a relative factor between 10% and 30%. https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-10--claim-test https://w3id.org/delphi/ontology/failCriterion Refutes if the intact fraction is < 0.83, or if the relative increase is negative. Mixed if the intact fraction is >= 0.83 but the relative increase is outside [0.10, 0.30] while still non-negative. Inconclusive with reason underpowered if fewer than 10 models have memory without noise. Inconclusive with reason data-unavailable if the models cannot be fetched. https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-10--claim-test https://w3id.org/delphi/ontology/hasConfidence 0.35 https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-10--claim-test https://w3id.org/delphi/ontology/hasEvidence https://w3id.org/delphi/resource/evidence/levin-2022-grn-learning-10--claim-test--dataset-1 https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-10--claim-test https://w3id.org/delphi/ontology/hasEvidence https://w3id.org/delphi/resource/evidence/levin-2022-grn-learning-10--claim-test--dataset-2 https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-10--claim-test https://w3id.org/delphi/ontology/hasVerdict inconclusive https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-10--claim-test https://w3id.org/delphi/ontology/hypothesis Stochastic noise leaves memories intact in at least 83% of the pathway models that had memory, and increases memory amount overall by about 20%. https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-10--claim-test https://w3id.org/delphi/ontology/inferenceMethod Paired comparison of memory labels and memory amount with and without noise. Intact fraction against 0.83; relative change of the mean against the interval [0.10, 0.30]. Both bars are required for supports. https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-10--claim-test https://w3id.org/delphi/ontology/passCriterion Pre-registered before any model file or simulation output is downloaded. The pass bars are the fractions stated in the claim. Intact: among models that have at least one memory kind without noise, the fraction that still have at least one memory kind when stochastic noise is added is >= 0.83. Increase: (mean memory amount with noise minus mean memory amount without noise) divided by mean memory amount without noise, over those models, is about 20%, fixed here as the closed interval from 0.10 to 0.30. Memory amount and memory kinds are those in the levin-2022-grn-learning-01 preregistration. Noise is the stochastic term in the authors' GRN_ODE noise assay if that code runs; otherwise independent multiplicative log-normal noise on production rates with sigma fixed in the analysis script before any noisy trajectory is scored. 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