{"id":"W2088033508","doi":"10.3354/meps08667","title":"Modeling the spatial autocorrelation of pelagic fish abundance","year":2010,"lang":"en","type":"article","venue":"Marine Ecology Progress Series","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Cooperative Institute for Marine and Atmospheric Studies, University of Miami; National Oceanic and Atmospheric Administration","keywords":"Pelagic zone; Swordfish; Fishery; Yellowfin tuna; Thunnus; Fishing; Geography; Oceanography; Miami; Tuna; Spatial analysis; Abundance (ecology); Ecology; Environmental science; Fish <Actinopterygii>; Biology; Geology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0007660026,0.0003924806,0.0003232867,0.0006457603,0.000332287,0.0006774095,0.000942505,0.0006513539,0.001028609],"category_scores_gemma":[0.002695421,0.0004363623,0.0007357239,0.0007369916,0.00043573,0.0006355669,0.0005549888,0.0004415788,0.0001371518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00126732,"about_ca_system_score_gemma":0.001108618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1039037,"about_ca_topic_score_gemma":0.06554028,"domain_scores_codex":[0.9997414,0.00006304147,0.00001567944,0.000104441,0.00002006948,0.00005541457],"domain_scores_gemma":[0.9988862,0.000594686,0.0002657604,0.00008439256,0.0001026525,0.00006634688],"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":[0.00002709859,0.00001689488,0.03861756,0.000008539516,0.00005444172,0.00004794286,0.00005139664,0.9566237,0.0003127842,0.001508825,0.0001478438,0.002583046],"study_design_scores_gemma":[0.00000261437,0.000005490225,0.003745696,0.000002124373,0.000007103523,0.000008778091,0.0000161909,0.9955543,0.00005507444,0.0005017419,0.00009770434,0.000003225143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9622744,0.0001417342,0.03582392,0.0001912985,0.00001223751,0.00001222873,0.0004356697,0.0001477458,0.0009606766],"genre_scores_gemma":[0.994692,0.0000540338,0.004237273,0.00001295433,0.000005810599,0.00001222966,0.0002089798,0.00001620915,0.0007604068],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1039037,"threshold_uncertainty_score":0.2065978,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008392183077649447,"score_gpt":0.2355844758544538,"score_spread":0.2271922927768044,"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."}}