{"id":"W2587906315","doi":"10.7554/elife.23804","title":"Towards a mechanistic foundation of evolutionary theory","year":2017,"lang":"en","type":"article","venue":"eLife","topic":"Evolutionary Game Theory and Cooperation","field":"Social Sciences","cited_by":117,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Fondo Nacional de Desarrollo Científico y Tecnológico; Natural Sciences and Engineering Research Council of Canada; John Simon Guggenheim Memorial Foundation","keywords":"Evolutionary dynamics; Fitness landscape; Context (archaeology); Human-based evolutionary computation; Evolutionary algorithm; Evolutionary ecology; Evolutionary theory; Evolutionary biology; Survival of the fittest; Biology; Computer science; Interactive evolutionary computation; Evolutionary programming; Artificial intelligence; Ecology; Epistemology; Population; Sociology; Demography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009936772,0.00004679713,0.00007354889,0.00002890385,0.001287865,0.000042096,0.0002526297,0.00005219597,0.0008239218],"category_scores_gemma":[0.001111487,0.00004698411,0.00003668936,0.0000407198,0.0003265906,0.0003596936,0.00003885997,0.00004704613,0.0001321163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005780412,"about_ca_system_score_gemma":0.0002660726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000321935,"about_ca_topic_score_gemma":0.0001751655,"domain_scores_codex":[0.9991517,0.0002277276,0.0001192184,0.0001004094,0.0002895243,0.000111391],"domain_scores_gemma":[0.9993688,0.00006241443,0.0001219495,0.0002266832,0.000177114,0.00004297803],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003225192,0.00002712332,0.000346742,0.000003237105,0.000007004351,5.385685e-7,0.001271872,0.000008538112,0.0004123549,0.9898494,0.0002950205,0.007745875],"study_design_scores_gemma":[0.0003827534,0.0001071466,0.05961895,0.00004918935,0.00003771994,0.000001782123,0.002552952,0.0005079404,0.001428623,0.8802054,0.05488741,0.0002201619],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3321906,0.0003384053,0.03675638,0.003132025,0.001559414,0.0004430203,0.00001864283,0.0001486324,0.6254129],"genre_scores_gemma":[0.9951017,0.0000684543,0.0006394653,0.00007187483,0.0002923457,0.000009995165,0.000008187683,0.000003978989,0.00380392],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6629112,"threshold_uncertainty_score":0.9905349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03017378850299324,"score_gpt":0.3381003437013417,"score_spread":0.3079265551983484,"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."}}