{"id":"W2167232595","doi":"10.1093/icesjms/fsr038","title":"Predicting habitat to optimize sampling of Pacific sardine (Sardinops sagax)","year":2011,"lang":"en","type":"article","venue":"ICES Journal of Marine Science","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":107,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Southwest Fisheries Science Center; Fundação para a Ciência e a Tecnologia; National Oceanic and Atmospheric Administration","keywords":"Sardine; Fishery; Pelagic zone; Habitat; Oceanography; Stock assessment; Environmental science; Sampling (signal processing); Generalized additive model; Sea surface temperature; Geography; Fish <Actinopterygii>; Ecology; Biology; Fishing; Geology; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00261,0.0001318976,0.0002798596,0.0002290291,0.0001778221,0.00006912565,0.001212602,0.00003208699,0.004184263],"category_scores_gemma":[0.0006227134,0.000103425,0.0000978086,0.001150593,0.0006818052,0.0009670994,0.001737973,0.0002560856,0.00003263636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001108972,"about_ca_system_score_gemma":0.00007249685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003729549,"about_ca_topic_score_gemma":0.00004936575,"domain_scores_codex":[0.9974544,0.00004379852,0.0005623102,0.0002723854,0.001207252,0.0004598312],"domain_scores_gemma":[0.9987144,0.00009638342,0.000354738,0.0003143226,0.0001467905,0.0003732991],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001263911,0.00007301338,0.9171159,0.0000120232,0.00001018009,0.00001862205,0.0008710673,0.0005380936,0.009460186,0.00002440325,0.0001439225,0.07160614],"study_design_scores_gemma":[0.0007511362,0.001591698,0.9664547,0.00006392845,0.00003564623,0.0002946855,0.001902294,0.00168241,0.01596597,0.0009545251,0.009923514,0.0003795171],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6839964,0.00000644694,0.0009599862,0.000137428,0.0002101953,0.0001035588,0.000001783776,0.000007941109,0.3145763],"genre_scores_gemma":[0.9388101,0.00004453024,0.06058535,0.00003652888,0.00007130604,0.000001768032,2.229282e-7,0.00000928186,0.000440884],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3141354,"threshold_uncertainty_score":0.996726,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04393248676287344,"score_gpt":0.2760776181300816,"score_spread":0.2321451313672082,"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."}}