{"id":"W3200721675","doi":"10.1111/ecog.05651","title":"<i>WiBB</i> : an integrated method for quantifying the relative importance of predictive variables","year":2021,"lang":"en","type":"article","venue":"Ecography","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Resampling; Weighting; Computer science; Generalized linear model; Regression; Linear discriminant analysis; Statistics; Linear regression; Data mining; Artificial intelligence; Machine learning; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00965706,0.001601146,0.00145289,0.006224678,0.0005839148,0.002125451,0.001736316,0.00155215,0.003123377],"category_scores_gemma":[0.03120569,0.0005522602,0.001547621,0.006132687,0.001778242,0.002573221,0.001873635,0.002541448,0.001314405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008368906,"about_ca_system_score_gemma":0.001563827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003584667,"about_ca_topic_score_gemma":0.00330271,"domain_scores_codex":[0.9961749,0.001668291,0.0003137882,0.0006240112,0.001077643,0.0001413063],"domain_scores_gemma":[0.9897636,0.006167875,0.001372387,0.001342556,0.001156042,0.0001975876],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004897463,0.0002312108,0.02379888,0.0009560333,0.0012916,0.0002715776,0.0004884768,0.09822503,0.02012742,0.08896544,0.02815645,0.7369981],"study_design_scores_gemma":[0.00004608006,0.0001497641,0.01258153,0.0002178708,0.0002139717,0.0002615485,0.000101199,0.8653224,0.0099136,0.09275055,0.0182414,0.0002001611],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00314644,0.0002642978,0.9942316,0.0001240806,0.00007414823,0.00005612669,0.0003416055,0.001092958,0.0006688336],"genre_scores_gemma":[0.08956678,0.0004177865,0.9056154,0.0003234076,0.0001739092,0.0005339959,0.001106634,0.0007710462,0.001490998],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00965706,"threshold_uncertainty_score":0.051072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02135871562341951,"score_gpt":0.2903829338925651,"score_spread":0.2690242182691456,"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."}}