{"id":"W3107756774","doi":"10.1007/s12064-020-00333-3","title":"Quantifying simultaneous innovations in evolutionary medicine","year":2020,"lang":"en","type":"article","venue":"Theory in Biosciences","topic":"Evolution and Genetic Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kellogg's (Canada)","funders":"National Science Foundation","keywords":"Philosophy of biology; Metadata; Perspective (graphical); Data science; Field (mathematics); Citation; Knowledge management; Citation analysis; Work (physics); Computer science; Sociology; Epistemology; Philosophy of science; World Wide Web; Artificial intelligence; Engineering","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":[0.0003503214,0.00007340557,0.00008209499,0.00008267919,0.00004896697,0.000005447675,0.0002044556,0.0000570568,0.00003855337],"category_scores_gemma":[0.0007249805,0.00006331031,0.0000139453,0.0006409,0.0003292673,0.000003734792,0.00006010467,0.00006796132,0.000008370916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001096764,"about_ca_system_score_gemma":0.00006168451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001766392,"about_ca_topic_score_gemma":0.0001763151,"domain_scores_codex":[0.999219,0.00007922528,0.0001958686,0.0002406604,0.0001091327,0.0001561046],"domain_scores_gemma":[0.999736,0.00004752708,0.00003927418,0.0001013051,0.00003254034,0.00004332911],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001676799,0.0001225672,0.076195,0.00002909105,0.000008059166,0.0000194178,0.001787666,0.0222263,0.8216005,0.07309539,0.0007313702,0.00401698],"study_design_scores_gemma":[0.00597248,0.003712823,0.3299645,0.0003117654,0.00003101486,0.00009216443,0.02789558,0.5013252,0.02079067,0.05497955,0.05261645,0.002307811],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9901277,0.0007001177,0.003081833,0.003430442,0.000125681,0.0001057952,0.000006265598,0.00001295966,0.002409158],"genre_scores_gemma":[0.997687,0.0001123561,0.0005772606,0.00141637,0.00007188847,0.00000406078,0.00002173819,0.000004037416,0.000105244],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8008098,"threshold_uncertainty_score":0.2581719,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03225892509118304,"score_gpt":0.308702598001444,"score_spread":0.2764436729102609,"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."}}