{"id":"W3153652063","doi":"10.1007/s00027-021-00797-5","title":"Modeling fish habitat: model tuning, fit metrics, and applications","year":2021,"lang":"en","type":"article","venue":"Aquatic Sciences","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University; Fisheries and Oceans Canada","funders":"Natural Sciences and Engineering Research Council of Canada; Great Lakes Fishery Commission","keywords":"Weighting; Sensitivity (control systems); Occupancy; Range (aeronautics); Species distribution; Statistics; Computer science; Data mining; Habitat; Ecology; Environmental science; Mathematics; Engineering","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":[0.00943641,0.000948896,0.001418497,0.001356884,0.000632882,0.00175058,0.001862634,0.001740806,0.001000337],"category_scores_gemma":[0.04070365,0.0007172067,0.0009760946,0.001332148,0.0009929124,0.003355722,0.00182873,0.001197893,0.0001324431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001613539,"about_ca_system_score_gemma":0.000973126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01738997,"about_ca_topic_score_gemma":0.01581939,"domain_scores_codex":[0.9976932,0.001523028,0.00009841567,0.0003889892,0.00020589,0.00009044861],"domain_scores_gemma":[0.986516,0.01043361,0.001029541,0.001093374,0.000657569,0.0002699641],"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":[0.00009041151,0.00007984055,0.02689165,0.00006106521,0.0001831234,0.00003532138,0.0001045122,0.9315349,0.0005424107,0.005949678,0.0006564559,0.03387055],"study_design_scores_gemma":[0.000007585327,0.0000288077,0.00179942,0.000009924373,0.00002198866,0.00002749301,0.000024057,0.9906452,0.0001349225,0.007141026,0.0001490967,0.00001056226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2884152,0.001359514,0.706883,0.000964712,0.00004071934,0.00008145582,0.0002889956,0.0006428185,0.001323572],"genre_scores_gemma":[0.9172338,0.0004080069,0.08113071,0.0001183913,0.0000348498,0.00009561179,0.0002254331,0.0002087615,0.0005444314],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01738997,"threshold_uncertainty_score":0.04990512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04352707937931272,"score_gpt":0.2690669218074102,"score_spread":0.2255398424280974,"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."}}