{"id":"W4221026757","doi":"10.1111/ele.13994","title":"PERFICT: A Re‐imagined foundation for predictive ecology","year":2022,"lang":"en","type":"article","venue":"Ecology Letters","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University; University of Victoria; Environment and Climate Change Canada; Université Laval; Natural Resources Canada; Western Forest Products; Government of British Columbia; University of British Columbia; Canadian Forest Service","funders":"Natural Resources Canada; Environment and Climate Change Canada; Natural Sciences and Engineering Research Council of Canada; Mitacs; fRI Research","keywords":"Workflow; Interoperability; Computer science; Ecology; Foundation (evidence); Data science; Management science; Quality (philosophy); Engineering; Geography; 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000198741,0.00008314657,0.0001011199,0.00003090118,0.0004394538,0.00001066211,0.0001751003,0.00003229981,0.1819618],"category_scores_gemma":[0.00004199451,0.0000927607,0.00005461459,0.0001174754,0.0001746613,0.00007833311,0.0001969035,0.0001095288,0.0006635651],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00140006,"about_ca_system_score_gemma":0.000008642116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002999887,"about_ca_topic_score_gemma":0.0004107438,"domain_scores_codex":[0.9991334,0.00007697222,0.0001253124,0.0002678747,0.0001073132,0.0002890919],"domain_scores_gemma":[0.9996668,0.00009193493,0.00006844188,0.0001257941,0.00000544068,0.0000415881],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0002159756,0.0002663929,0.3268541,0.00000592529,0.00004786486,0.00001836697,0.0008560665,0.001741191,0.01713257,0.0007291267,0.6515964,0.0005360676],"study_design_scores_gemma":[0.0009223644,0.000380335,0.7301143,2.374652e-7,0.00002645392,0.00001549201,0.001346219,0.001367673,0.0001423833,0.0001343067,0.2654043,0.0001459292],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9599267,0.000002014519,0.0004348,0.03177409,0.0008439524,0.0004465718,0.0001041149,0.00006349007,0.006404242],"genre_scores_gemma":[0.9839082,0.000002255791,0.0001082665,0.01435441,0.00004404833,0.0006301487,0.0003332371,0.00001060868,0.0006087885],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4032603,"threshold_uncertainty_score":0.8529009,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01560046918841982,"score_gpt":0.2356970226095501,"score_spread":0.2200965534211303,"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."}}