{"id":"W2041076167","doi":"10.1139/f03-032","title":"Quantifying habitat associations in marine fisheries: a generalization of the KolmogorovSmirnov statistic using commercial logbook records linked to archived environmental data","year":2003,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. Department of Commerce","keywords":"Rockfish; Fishery; Sebastes; Habitat; Logbook; Fishing; Environmental science; Statistic; Environmental data; Abundance (ecology); Sampling (signal processing); Oceanography; Geography; Ecology; Fish <Actinopterygii>; Biology; Statistics; Geology; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02358157,0.0006987842,0.0008738656,0.006116757,0.001109484,0.002633348,0.00159321,0.00109205,0.00122404],"category_scores_gemma":[0.1170419,0.0004259483,0.001689571,0.006545947,0.004079052,0.005363721,0.003684053,0.001545617,0.000238511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001196699,"about_ca_system_score_gemma":0.001339263,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004836951,"about_ca_topic_score_gemma":0.003925435,"domain_scores_codex":[0.9804776,0.009490026,0.002590666,0.003378643,0.003472852,0.0005902992],"domain_scores_gemma":[0.8859252,0.08659203,0.01284237,0.01109652,0.002468906,0.001074975],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002233228,0.0001166009,0.8918321,0.0001861054,0.0006727122,0.0002589792,0.001422554,0.01513109,0.00154203,0.01272125,0.0009481056,0.07494505],"study_design_scores_gemma":[0.00004986796,0.001073453,0.7548439,0.0001848718,0.000201836,0.00123687,0.003621799,0.1646451,0.0032963,0.06559326,0.005034701,0.0002180647],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6519047,0.0004422129,0.3400738,0.0002826597,0.00008256832,0.0002581662,0.001637583,0.0005470816,0.004771285],"genre_scores_gemma":[0.949832,0.00008539153,0.04856575,0.0000740516,0.00004135452,0.0002849385,0.0008107101,0.00008242953,0.0002234389],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02358157,"threshold_uncertainty_score":0.1247128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08834747496471188,"score_gpt":0.2873612155585478,"score_spread":0.1990137405938359,"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."}}