{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005628545,0.0002899938,0.0002985883,0.0005192633,0.0006153786,0.001187386,0.0003989109,0.0007286289,0.002126386],"category_scores_gemma":[0.002902273,0.0001375573,0.000385974,0.00025673,0.001606928,0.001071883,0.00110351,0.0006726364,0.0001968289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001021235,"about_ca_system_score_gemma":0.0004583432,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001146053,"about_ca_topic_score_gemma":0.0007889858,"domain_scores_codex":[0.9997966,0.00009063004,0.000005684666,0.0000380793,0.00003743006,0.00003157818],"domain_scores_gemma":[0.9993042,0.0003514837,0.0001508997,0.00006667384,0.00005158985,0.00007515626],"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":[0.00001793437,0.00001281104,0.002252045,0.00004235806,0.00001968056,0.0001332734,0.0001448359,0.1570127,0.003923571,0.8294746,0.0006742271,0.006291813],"study_design_scores_gemma":[0.00001108265,0.00005341996,0.001902507,0.00001695866,0.0000099473,0.0001840272,0.00009128908,0.4614224,0.0008285974,0.5318066,0.003651751,0.00002127407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5601851,0.001702327,0.3813845,0.005809156,0.0001484543,0.00004386756,0.0001474821,0.0001974907,0.05038164],"genre_scores_gemma":[0.9858526,0.0004641211,0.01034812,0.0001118034,0.00003561774,0.00003386935,0.00003604611,0.00003009589,0.00308765],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002126386,"threshold_uncertainty_score":0.007409632,"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."}}