{"id":"W4321795051","doi":"10.1177/11769351231154679","title":"Cell Adaptive Fitness and Cancer Evolutionary Dynamics","year":2023,"lang":"en","type":"article","venue":"Cancer Informatics","topic":"Mathematical Biology Tumor Growth","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Genome instability; Biology; In silico; Evolutionary dynamics; Cancer cell; Context (archaeology); Fitness landscape; Computational biology; Evolutionary biology; Cancer; Genetics; Gene; Medicine; DNA","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":[],"consensus_categories":[],"category_scores_codex":[0.0002590821,0.0002247569,0.0003407632,0.0001320458,0.0001479614,0.00002679161,0.0002305648,0.0001477977,0.0002167261],"category_scores_gemma":[0.0001434527,0.000189907,0.00005841602,0.0004262516,0.0002096473,0.0002254606,0.0002154969,0.0002371003,0.000125662],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002279604,"about_ca_system_score_gemma":0.000134826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002761886,"about_ca_topic_score_gemma":0.00003281911,"domain_scores_codex":[0.9986337,0.00002924986,0.0005352797,0.0001343448,0.0002529428,0.0004144966],"domain_scores_gemma":[0.9987356,0.0004772274,0.0002356486,0.0002900431,0.0001263225,0.0001351542],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000115338,0.0001935179,0.01343563,0.006371039,0.0002547728,0.00003053127,0.01580413,0.0005054941,0.00008105436,0.6884249,0.2569439,0.01783968],"study_design_scores_gemma":[0.001560806,0.0001485724,0.003444591,0.0005153206,0.0002308021,0.00003403781,0.007212535,0.5636715,0.000645569,0.4151435,0.006343212,0.00104954],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9342762,0.001278858,0.0131235,0.001381196,0.001086816,0.001312293,0.001096547,0.001097788,0.04534682],"genre_scores_gemma":[0.9480149,0.002757224,0.03601832,0.001109525,0.000410038,0.001133252,0.0001429294,0.0001433429,0.01027045],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.563166,"threshold_uncertainty_score":0.7744182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04276011452890718,"score_gpt":0.3136605283647698,"score_spread":0.2709004138358626,"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."}}