{"id":"W2462372215","doi":"10.1111/ecog.02302","title":"Linking trait variation to the environment: critical issues with community‐weighted mean correlation resolved by the fourth‐corner approach","year":2016,"lang":"en","type":"article","venue":"Ecography","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":170,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Trait; Variation (astronomy); Correlation; Ecology; Centroid; Statistics; Niche; Computer science; Mathematics; Econometrics; Biology; Artificial intelligence; Physics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005242543,0.0001366717,0.00009109287,0.00002715965,0.0006899219,0.00007223123,0.0003521741,0.00006251813,0.006245194],"category_scores_gemma":[0.00002127291,0.00006309917,0.00006382716,0.0003085028,0.0003399806,0.0001602848,0.0001071545,0.0001842729,0.0004455259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001050419,"about_ca_system_score_gemma":0.000001824138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001904172,"about_ca_topic_score_gemma":0.0002118255,"domain_scores_codex":[0.9987528,0.0002743484,0.0001574583,0.0002102562,0.0003484709,0.0002567118],"domain_scores_gemma":[0.999227,0.0002091705,0.00005412782,0.0004204918,0.000008554092,0.00008067744],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008890916,0.004580006,0.404413,0.00008622285,0.0005361906,0.000007882431,0.08575556,0.0009454562,0.04568919,0.06872509,0.2368204,0.1515519],"study_design_scores_gemma":[0.001006018,0.000360365,0.6305445,0.00004804196,0.0001084052,0.000008946139,0.005490841,0.001043244,0.0007156449,0.001898234,0.3582655,0.0005101552],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7014048,0.0002061048,0.2157606,0.02798646,0.0002711192,0.001326106,0.0004146874,0.0002160649,0.05241412],"genre_scores_gemma":[0.9982141,0.00003008664,0.0004948825,0.0007411263,0.00003696963,0.00007617362,0.0001171977,0.00001446335,0.0002749603],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2968094,"threshold_uncertainty_score":0.9946632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02180890531796603,"score_gpt":0.2250255965370254,"score_spread":0.2032166912190593,"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."}}