{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03818016,0.001748244,0.001633868,0.005074381,0.003196926,0.01516437,0.006676514,0.006476419,0.01401664],"category_scores_gemma":[0.07833385,0.001491417,0.002988691,0.003189404,0.0142573,0.03101549,0.01202006,0.02087953,0.006500154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0041255,"about_ca_system_score_gemma":0.01291661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008655304,"about_ca_topic_score_gemma":0.004506987,"domain_scores_codex":[0.988101,0.003761001,0.001230068,0.00154671,0.004617504,0.0007436562],"domain_scores_gemma":[0.9329032,0.02523728,0.002075269,0.02449636,0.01155471,0.00373318],"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":[0.00004855185,0.00007789747,0.0007569598,0.0002164172,0.00003929356,0.00008518979,0.0002761121,0.007163684,0.0003158636,0.9062433,0.02823248,0.05654423],"study_design_scores_gemma":[0.00002845126,0.00004619329,0.0002595672,0.0005394361,0.00002489171,0.00009871527,0.00010306,0.02711745,0.000653961,0.8102338,0.1608212,0.00007328351],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002089259,0.001835021,0.9191816,0.04376995,0.002602686,0.0001885833,0.00149426,0.005397303,0.02344137],"genre_scores_gemma":[0.06935819,0.005370263,0.8992363,0.007150926,0.003271503,0.0008285341,0.003932877,0.003191338,0.007660068],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03818016,"threshold_uncertainty_score":0.2019184,"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."}}