{"id":"W4366409718","doi":"10.1002/mcf2.10235","title":"Trends in Area of Occurrence and Biomass of Fish and Macroinvertebrates on the Northeast U.S. Shelf Ecosystem","year":2023,"lang":"en","type":"article","venue":"Marine and Coastal Fisheries","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada","funders":"","keywords":"Biomass (ecology); Ecosystem; Environmental science; Invertebrate; Productivity; Marine ecosystem; Ecology; Population; Habitat; Taxon; Climate change; Spatial distribution; Fishery; Geography; Physical geography; Oceanography; 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001912448,0.000113472,0.0001870505,0.00007685663,0.00006479636,0.00003551152,0.0001072241,0.00003560699,0.001208637],"category_scores_gemma":[0.00003140643,0.00007925792,0.00002053037,0.0004747279,0.0004048694,0.0001233913,0.0008298217,0.000084636,0.000002168739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005689134,"about_ca_system_score_gemma":0.000005088773,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00289191,"about_ca_topic_score_gemma":0.02263579,"domain_scores_codex":[0.9991938,0.00004069624,0.0001907905,0.0002096041,0.0001803469,0.0001847558],"domain_scores_gemma":[0.9996434,0.00009994122,0.00004753325,0.0001494015,0.00000803505,0.00005170637],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005099505,0.00002029446,0.8791553,0.00005773495,0.00000624908,0.000006879265,0.0002515479,3.965056e-7,0.000167092,0.0000552722,0.002182586,0.1180457],"study_design_scores_gemma":[0.0003309101,0.0003137285,0.9782755,0.00001968928,0.00000539083,0.00001055727,0.001199564,0.001625858,0.0005325158,0.0003310388,0.01721251,0.0001426798],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9533849,0.000003420712,4.755877e-7,0.001412512,0.00001645451,0.0000808023,0.0001302188,0.00001082944,0.04496042],"genre_scores_gemma":[0.9985855,0.0001594021,0.000006958679,0.00005770462,0.000004959582,0.00001367087,0.00003049457,0.000005390183,0.001135895],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.117903,"threshold_uncertainty_score":0.9997044,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01861903637032286,"score_gpt":0.2203428707486748,"score_spread":0.201723834378352,"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."}}