{"id":"W4206611579","doi":"10.1002/9780470057339.vaf006","title":"Fisheries","year":2006,"lang":"en","type":"other","venue":"Encyclopedia of Environmetrics","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Stock assessment; Fishery; Fisheries management; Variety (cybernetics); Stock (firearms); Estimation; Fish stock; Population; Fish <Actinopterygii>; Statistical model; Abundance estimation; Computer science; Geography; Abundance (ecology); Statistics; Fishing; Mathematics; Engineering; Biology; Machine learning","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":[0.0004657976,0.0008209558,0.0003348112,0.002449672,0.0008730681,0.002401032,0.0007445617,0.0006703527,0.3788535],"category_scores_gemma":[0.0006558613,0.0002089761,0.0004671506,0.001948341,0.0002611351,0.001164651,0.001669214,0.0008604385,0.245203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009090413,"about_ca_system_score_gemma":0.001232544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01099903,"about_ca_topic_score_gemma":0.01284667,"domain_scores_codex":[0.9996755,0.0000225245,0.00002663126,0.00006425768,0.0001679803,0.00004306],"domain_scores_gemma":[0.9996864,0.00001718764,0.00002127481,0.00005303949,0.0001716913,0.00005035648],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006363011,0.00004310223,0.001504461,0.0002025677,0.00001642407,0.000126915,0.00005930481,0.0002881354,0.0009050341,0.01543607,0.6276269,0.3537275],"study_design_scores_gemma":[0.000005114196,0.000007541759,0.001373013,0.00004737719,0.00000241004,0.00004834947,0.00002579271,0.0000884877,0.0001436171,0.001306123,0.9969485,0.000003665828],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.001766242,0.002096304,0.002221043,0.002195612,0.001502225,0.0001088389,0.02722356,0.001352688,0.9615334],"genre_scores_gemma":[0.0103784,0.001933652,0.002314139,0.0007710889,0.0003307925,0.00007407422,0.02500644,0.0003262929,0.9588652],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.3788535,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007859733773830698,"score_gpt":0.2083488949114352,"score_spread":0.2004891611376045,"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."}}