{"id":"W2084224700","doi":"10.1890/04-0642","title":"RANGE CONTRACTION MAY NOT ALWAYS PREDICT CORE AREAS: AN EXAMPLE FROM MARINE FISH","year":2005,"lang":"en","type":"article","venue":"Ecological Applications","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bedford Institute of Oceanography; Fisheries and Oceans Canada","funders":"","keywords":"Fishing; Range (aeronautics); Population density; Ecology; Density dependence; Abundance (ecology); Population; Geography; Fishery; Population dynamics of fisheries; Environmental science; Fish <Actinopterygii>; Physical geography; Biology; Demography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001156329,0.0002490306,0.0003335849,0.001194344,0.0009035191,0.0006040688,0.0004656108,0.0003400964,0.0008182298],"category_scores_gemma":[0.004908996,0.0001323899,0.0002979761,0.001266957,0.0009731163,0.0004420016,0.0008832176,0.0003804688,0.0001406015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009492415,"about_ca_system_score_gemma":0.0006958785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1585612,"about_ca_topic_score_gemma":0.2770433,"domain_scores_codex":[0.999676,0.00006508353,0.00002221886,0.00009725989,0.00007036563,0.00006919706],"domain_scores_gemma":[0.9974772,0.001112174,0.0003576381,0.0002686236,0.0005856142,0.0001986797],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006442339,0.00001350978,0.9869465,0.00002924868,0.00001710909,0.0004387535,0.0005913366,0.002124705,0.001657258,0.0003940022,0.0001569184,0.007566246],"study_design_scores_gemma":[0.000005328693,0.00005663972,0.9816342,0.00001877015,0.00002211529,0.0009013897,0.00114853,0.01329031,0.0006234015,0.001159909,0.001124329,0.00001505877],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.994576,0.0001939127,0.002828543,0.00006525922,0.00000232581,0.00001845287,0.0001458737,0.00001751988,0.002152149],"genre_scores_gemma":[0.9977435,0.00004795957,0.001819658,0.00001798558,0.000001371957,0.00000593733,0.0001858892,0.000005865796,0.0001717033],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1585612,"threshold_uncertainty_score":0.3152765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05166598764263833,"score_gpt":0.273036163780196,"score_spread":0.2213701761375577,"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."}}