{"id":"W2982546457","doi":"10.3389/fevo.2019.00402","title":"Artificial Intelligence for Ecological and Evolutionary Synthesis","year":2019,"lang":"en","type":"article","venue":"Frontiers in Ecology and Evolution","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies; Nvidia","keywords":"Ecology; Evolutionary ecology; Theoretical ecology; Computer science; Biodiversity; Population; Artificial intelligence; Sociology; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003882601,0.001383306,0.001641992,0.002907099,0.001206762,0.005763358,0.001758053,0.002874341,0.0124242],"category_scores_gemma":[0.008850421,0.0004788595,0.00126137,0.002417132,0.007968442,0.008071053,0.003002508,0.00480301,0.00194664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002892498,"about_ca_system_score_gemma":0.002221039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001894436,"about_ca_topic_score_gemma":0.001479469,"domain_scores_codex":[0.9977615,0.0012137,0.0001511123,0.0003379167,0.0004419334,0.00009386706],"domain_scores_gemma":[0.9961421,0.00266742,0.0001709482,0.0005843689,0.0003237327,0.0001114711],"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":[0.000006849792,0.0000102786,0.0001056287,0.0001945331,0.0000306114,0.00003660017,0.0001258363,0.003226789,0.00008353155,0.9778551,0.004327194,0.01399711],"study_design_scores_gemma":[0.000002606615,0.000002889283,0.00004333434,0.00007221946,0.000004621086,0.00001206072,0.00003001859,0.00211725,0.00002238921,0.9794441,0.01824391,0.000004576702],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006426629,0.1366006,0.4929094,0.05506594,0.003686826,0.0002191476,0.001024871,0.0007097382,0.303357],"genre_scores_gemma":[0.4513349,0.1057634,0.3783812,0.008588638,0.006616607,0.001366297,0.001786281,0.0005358246,0.04562693],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0124242,"threshold_uncertainty_score":0.04156309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01725222739310367,"score_gpt":0.2352556718877735,"score_spread":0.2180034444946699,"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."}}