{"id":"W2031488572","doi":"10.1016/j.ecolmodel.2011.11.003","title":"Modelling commercial fish distributions: Prediction and assessment using different approaches","year":2011,"lang":"en","type":"article","venue":"Ecological Modelling","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":133,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"University of British Columbia; Natural Environment Research Council; Sight Research UK; Department for Environment, Food and Rural Affairs, UK Government","keywords":"Robustness (evolution); Computer science; Species distribution; Environmental niche modelling; Habitat; Ecology; Statistics; Mathematics; Biology; Ecological niche","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":[0.001469565,0.0006708551,0.0006068049,0.0007118647,0.000415578,0.001153888,0.001610873,0.0016156,0.001285978],"category_scores_gemma":[0.004005685,0.0005516142,0.001121106,0.001040584,0.0006769148,0.001387173,0.0007184976,0.0005889665,0.000132761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001262133,"about_ca_system_score_gemma":0.00070384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03718266,"about_ca_topic_score_gemma":0.03230244,"domain_scores_codex":[0.999621,0.000144624,0.00003555004,0.00008668572,0.00006033129,0.00005187844],"domain_scores_gemma":[0.998137,0.001410297,0.0001288732,0.00007681299,0.0001775957,0.00006938304],"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":[0.00003090408,0.00002344234,0.003335264,0.00001530248,0.00003877035,0.00001479183,0.00002649638,0.9907215,0.0002126042,0.0009260987,0.00004384224,0.004611093],"study_design_scores_gemma":[0.000005376546,0.000009024488,0.0009307832,0.000001888474,0.00000929801,0.000004815994,0.000008767905,0.9980683,0.00009839898,0.0008223774,0.00003716421,0.000003782591],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6699497,0.0003669043,0.3251794,0.0003377358,0.00003318762,0.00006960157,0.0003356338,0.0001821294,0.003545629],"genre_scores_gemma":[0.9690371,0.0001590487,0.02939696,0.00002579517,0.00001408187,0.00007898857,0.0001637387,0.00002609227,0.001098242],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03718266,"threshold_uncertainty_score":0.07393247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2883310622982557,"score_gpt":0.2765197707423159,"score_spread":0.0118112915559398,"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."}}