{"id":"W2144749488","doi":"10.1016/j.ecolmodel.2009.11.008","title":"New approaches to modelling fish–habitat relationships","year":2009,"lang":"en","type":"article","venue":"Ecological Modelling","topic":"Coral and Marine Ecosystems Studies","field":"Environmental Science","cited_by":148,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Range (aeronautics); Species richness; Generalized linear model; Variable (mathematics); Random forest; Generalized additive model; Ecology; Linear model; Regression; Habitat; Computer science; Linear regression; Support vector machine; Statistics; Mathematics; Machine learning; Biology","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":[0.002166597,0.0009356813,0.001303789,0.001180937,0.0007362303,0.00214635,0.003960248,0.002221304,0.004148508],"category_scores_gemma":[0.008999622,0.001164097,0.002240601,0.001794437,0.001654909,0.004177867,0.002393822,0.002937979,0.0004303946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001513149,"about_ca_system_score_gemma":0.0009248957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01372826,"about_ca_topic_score_gemma":0.01573743,"domain_scores_codex":[0.9993399,0.0003297222,0.0000588364,0.00009469628,0.0001384072,0.00003838549],"domain_scores_gemma":[0.9966731,0.002471718,0.0002540883,0.0002371437,0.0002039995,0.0001598466],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001326862,0.00002324889,0.000764342,0.0000676007,0.0001019857,0.00004225292,0.00009560632,0.9317446,0.0003502851,0.05778548,0.0004759502,0.008535321],"study_design_scores_gemma":[0.000009114872,0.000004741766,0.0001074106,0.000007635615,0.00002293438,0.00001827365,0.00001627547,0.9594326,0.0000437151,0.03904283,0.001287225,0.000007305589],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01640531,0.0008709037,0.9777834,0.0007918492,0.0001527881,0.00002321752,0.000123561,0.00009156875,0.003757431],"genre_scores_gemma":[0.4936817,0.003827629,0.4856401,0.0005727896,0.0007601582,0.0005447498,0.0004535753,0.0003029969,0.01421617],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01372826,"threshold_uncertainty_score":0.02729672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.226309178386746,"score_gpt":0.2260926920837335,"score_spread":0.0002164863030124642,"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."}}