{"id":"W3041332680","doi":"10.1139/cjfas-2020-0076","title":"A framework to incorporate environmental effects into stock assessments informed by fishery-independent surveys: a case study with American lobster (<i>Homarus americanus</i>)","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Oceanic and Atmospheric Administration; Cooperative Institute for the North Atlantic Region; Northeast Fisheries Science Center; National Marine Fisheries Service; Atlantic States Marine Fisheries Commission","keywords":"American lobster; Homarus; Stock assessment; Stock (firearms); Fishery; Covariate; Abundance (ecology); Population; Ecology; Econometrics; Environmental science; Geography; Biology; Fishing; Economics; Crustacean","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":[0.01534635,0.000806485,0.0005712291,0.001344477,0.0007480935,0.001991741,0.001928865,0.001255981,0.001362848],"category_scores_gemma":[0.01832755,0.0007206392,0.00132318,0.001130542,0.001056046,0.002438259,0.002444972,0.00147178,0.0002302394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001525247,"about_ca_system_score_gemma":0.003584946,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02574049,"about_ca_topic_score_gemma":0.05183731,"domain_scores_codex":[0.9964179,0.002547364,0.0001589804,0.000434012,0.000308713,0.0001330822],"domain_scores_gemma":[0.9913344,0.005611271,0.0009662915,0.0007937775,0.001029744,0.0002644566],"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":[0.0001721155,0.000393255,0.05910144,0.0001878904,0.0004424929,0.0009162669,0.001380698,0.697563,0.003048938,0.1171025,0.00192229,0.1177692],"study_design_scores_gemma":[0.00004330157,0.0002629098,0.006418833,0.0001294925,0.00008482194,0.0001913454,0.0003794146,0.9306829,0.001140308,0.05444504,0.006138308,0.00008324973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03722925,0.00011309,0.958141,0.0009090214,0.00003694991,0.0001875158,0.0002910524,0.0003692464,0.002722921],"genre_scores_gemma":[0.2834563,0.0001103124,0.715083,0.000139935,0.00002328499,0.0001796705,0.00018689,0.00004570267,0.0007750631],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02574049,"threshold_uncertainty_score":0.08116019,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02057807553732708,"score_gpt":0.2669998072648056,"score_spread":0.2464217317274785,"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."}}