{"id":"W4397003240","doi":"10.1142/s0219720024500112","title":"Body plan evolvability: The role of variability in gene regulatory networks","year":2024,"lang":"en","type":"article","venue":"Journal of Bioinformatics and Computational Biology","topic":"Animal Genetics and Reproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"British Columbia Institute of Technology","funders":"","keywords":"Evolvability; Body plan; Gene regulatory network; Gene; Plan (archaeology); Biology; Computational biology; Regulator gene; Genetics; Evolutionary biology; Regulation of gene expression; Gene expression","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006212593,0.0001562101,0.0001719576,0.0002822603,0.0002326602,0.001186299,0.0004182093,0.0005462145,0.001376995],"category_scores_gemma":[0.003143097,0.000201156,0.0002612107,0.0001753888,0.001120616,0.001043354,0.000662891,0.0006179405,0.0001612888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004510072,"about_ca_system_score_gemma":0.0001728749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008120207,"about_ca_topic_score_gemma":0.0006051775,"domain_scores_codex":[0.9997218,0.0000767382,0.00001344951,0.000101172,0.00005275674,0.00003394421],"domain_scores_gemma":[0.9985785,0.0006732833,0.0003056801,0.0002950977,0.00007008491,0.00007731427],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003435978,0.00008788557,0.1057445,0.0001249149,0.0001788549,0.0006491636,0.0006378643,0.4613894,0.2795839,0.06981664,0.0008249054,0.08061843],"study_design_scores_gemma":[0.00003036725,0.0002206821,0.1178175,0.00001931838,0.00005090056,0.0005982032,0.0002690186,0.7481257,0.04015634,0.09025686,0.002393247,0.00006184377],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9341756,0.0001950079,0.06225009,0.0004179285,0.00001270801,0.000009101864,0.0001415371,0.0002664245,0.002531611],"genre_scores_gemma":[0.9966764,0.00004787923,0.002930562,0.00002066068,0.000003488017,0.000005859808,0.00003024357,0.00001542599,0.0002695217],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001376995,"threshold_uncertainty_score":0.004606485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004502595958025534,"score_gpt":0.2224353074338491,"score_spread":0.2179327114758235,"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."}}