{"id":"W3131377851","doi":"10.1101/2021.02.18.430807","title":"Relating simulation studies by provenance—Developing a family of Wnt signaling models","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Deutsche Forschungsgemeinschaft","keywords":"Computer science; Metadata; Ontology; Reuse; Variety (cybernetics); Simulation modeling; Exploit; Data modeling; Data science; Key (lock); Software engineering; World Wide Web; Engineering; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005863335,0.0004275336,0.0006376135,0.00008596907,0.0001253638,0.00006134956,0.0003422426,0.0007199004,0.000001551121],"category_scores_gemma":[0.0008238451,0.0004341007,0.000171208,0.0002365464,0.0002067947,0.00001115189,0.0006944979,0.0004184098,0.000001037598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009534966,"about_ca_system_score_gemma":0.0005783391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000191435,"about_ca_topic_score_gemma":8.465976e-7,"domain_scores_codex":[0.9975116,0.0001463916,0.000698557,0.0009101577,0.0003244734,0.0004087801],"domain_scores_gemma":[0.9978486,0.00008318436,0.000588014,0.0006471578,0.0007369597,0.00009604771],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002369631,0.0000491902,0.000336026,0.0005574684,0.0003376792,0.000008465075,0.00003985073,0.01038193,0.9880075,0.00007819904,0.0001525383,0.0000274665],"study_design_scores_gemma":[0.0004825294,0.0001210872,0.000943757,0.001659284,0.00009365581,1.434084e-8,0.0001147804,0.006578925,0.9876602,0.00001681035,0.001533696,0.0007952714],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9330136,0.02604121,0.0398558,0.00009195176,0.0005049347,0.0003241097,0.00006907032,0.00009281279,0.000006530322],"genre_scores_gemma":[0.9727383,0.001214505,0.02552625,0.0001429481,0.0002145813,0.00008442988,0.000003006701,0.00006903745,0.000006888402],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03972477,"threshold_uncertainty_score":0.9998111,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04067446743548804,"score_gpt":0.2783287480595395,"score_spread":0.2376542806240515,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}