{"id":"W2890887510","doi":"10.1111/eva.12711","title":"Comparison of coded‐wire tagging with parentage‐based tagging and genetic stock identification in a large‐scale coho salmon fisheries application in British Columbia, Canada","year":2018,"lang":"en","type":"article","venue":"Evolutionary Applications","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Fisheries and Oceans Canada","funders":"Fisheries and Oceans Canada; Genome British Columbia; Genome Canada","keywords":"Biology; Fishery; Stock (firearms); Scale (ratio); Identification (biology); Stock assessment; Ecology; Archaeology; Fishing; Geography; Cartography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.001362247,0.000447402,0.0002010015,0.0007785466,0.001100753,0.0008379123,0.0007427135,0.0002658691,0.001426426],"category_scores_gemma":[0.003173932,0.0002533022,0.000234161,0.001052899,0.0004498449,0.0002622018,0.0004092153,0.0002366122,0.0002746664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007274339,"about_ca_system_score_gemma":0.008044949,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9580708,"about_ca_topic_score_gemma":0.9856058,"domain_scores_codex":[0.9992468,0.0001264247,0.00004628997,0.0001950491,0.0002625406,0.0001228454],"domain_scores_gemma":[0.9975412,0.0004317901,0.0001933439,0.0001005648,0.001425556,0.0003075762],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004862513,0.0001517269,0.9289401,0.00009369421,0.0001766386,0.0001206954,0.001259127,0.001238929,0.0087495,0.0001104403,0.001650096,0.05702279],"study_design_scores_gemma":[0.00001907002,0.0001081988,0.9945397,0.0000214424,0.00005666861,0.00004135022,0.0009396133,0.002424584,0.0009823693,0.00002254765,0.0008315737,0.00001294113],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967701,0.0002114872,0.0006777395,0.00008163657,0.00001146587,0.00007903604,0.0004351028,0.00002754574,0.001705958],"genre_scores_gemma":[0.9943579,0.0002147198,0.0020414,0.0000657036,0.000003407558,0.00003745242,0.0008418069,0.0000133096,0.002424331],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04192924,"threshold_uncertainty_score":0.08435231,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006265690803603213,"score_gpt":0.2207621784423183,"score_spread":0.2144964876387151,"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."}}