{"id":"W2104132255","doi":"10.1046/j.1365-2427.2002.00945.x","title":"A comparison of statistical approaches for modelling fish species distributions","year":2002,"lang":"en","type":"article","venue":"Freshwater Biology","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":250,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Artificial neural network; Linear discriminant analysis; Discriminant function analysis; Predictive power; Generalized linear model; Ecology; Linear model; Statistical model; Logistic regression; Habitat; Fish <Actinopterygii>; Machine learning; Statistics; Artificial intelligence; Computer science; Biology; Mathematics; Fishery","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.00006729777,0.0000674016,0.0001605536,0.00001394586,0.0001153338,0.000003128135,0.000101384,0.0000536555,0.002520386],"category_scores_gemma":[0.00002169747,0.00005417769,0.0000273156,0.00003595121,0.0004082146,0.00002746218,0.0001204087,0.00004234272,0.0000484637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000195605,"about_ca_system_score_gemma":4.504458e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002123831,"about_ca_topic_score_gemma":0.001212683,"domain_scores_codex":[0.9994318,0.00002530725,0.0001554113,0.0001639705,0.00002973073,0.0001937671],"domain_scores_gemma":[0.9997782,0.00007012569,0.0000402599,0.00008818815,0.000003687107,0.00001955605],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002217266,0.0003337319,0.4614518,0.00002354696,0.00005160548,6.064722e-7,0.0005727919,0.004765227,0.0001559773,0.0367324,0.4951111,0.0007790508],"study_design_scores_gemma":[0.0005524259,0.0004211202,0.06240842,0.000003630374,0.00006470738,9.051258e-7,0.0002427829,0.3409279,0.001111349,0.01971998,0.5743005,0.0002462939],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1395893,0.00002996361,0.8418593,0.001944163,0.0001760979,0.0003913117,0.001503892,0.00003828196,0.01446777],"genre_scores_gemma":[0.9868248,0.00001266477,0.01189865,0.00009353828,0.00002022755,0.00005478348,0.0001888416,0.00000333721,0.0009031385],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8472356,"threshold_uncertainty_score":0.9983914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1064852866452082,"score_gpt":0.2749208602655958,"score_spread":0.1684355736203876,"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."}}