{"id":"W4297991912","doi":"10.1371/journal.pcbi.1010302","title":"Generalism drives abundance: A computational causal discovery approach","year":2022,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Plant and animal studies","field":"Agricultural and Biological Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; University of Toronto","funders":"Liber Ero Foundation; Royal Commission for the Exhibition of 1851","keywords":"Abundance (ecology); Generalist and specialist species; Structuring; Ecology; Computer science; Selection (genetic algorithm); Process (computing); Causal inference; Inference; Biology; Machine learning; Artificial intelligence; Habitat; Econometrics; Mathematics","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":[0.00009502251,0.0001341824,0.0001883494,0.00001866744,0.000743992,0.00003986617,0.000221175,0.00003563156,0.0002369188],"category_scores_gemma":[0.000013642,0.00006157557,0.00007673565,0.0002073756,0.000114414,0.00008057381,0.0002505732,0.0001450505,0.00001935849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003755405,"about_ca_system_score_gemma":0.00001736899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004409471,"about_ca_topic_score_gemma":0.00002737141,"domain_scores_codex":[0.99887,0.0001447638,0.0001932428,0.0003360278,0.0002209928,0.0002350146],"domain_scores_gemma":[0.9994584,0.000348276,0.00008541912,0.00001319825,0.00005096708,0.00004370697],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0004279332,0.002385197,0.2195527,0.00003332261,0.0006560129,0.00005230378,0.0009093375,0.3020633,0.1515723,0.2964208,0.01617205,0.009754824],"study_design_scores_gemma":[0.0006282334,0.0009118395,0.807129,0.000006603731,0.00003391971,0.0001454432,0.000646666,0.07382275,0.00007887493,0.09152237,0.0243394,0.0007348814],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959969,0.0002982962,0.0004030752,0.001294301,0.00008589141,0.0001383041,0.0007666851,0.00007715996,0.0009393682],"genre_scores_gemma":[0.9946566,0.000008185439,0.001712773,0.0008156194,0.0002903502,0.00007130604,0.002246018,0.00000110183,0.000198062],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5875763,"threshold_uncertainty_score":0.572226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05363092813265379,"score_gpt":0.2258945690696758,"score_spread":0.1722636409370221,"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."}}