{"id":"W1583622709","doi":"10.1111/cobi.12229","title":"Catching Up on Fisheries Crime","year":2014,"lang":"en","type":"article","venue":"Conservation Biology","topic":"Identification and Quantification in Food","field":"Biochemistry, Genetics and Molecular Biology","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Fishing; Fish stock; Fisheries management; Business; Fishery; Corporate governance; International waters; Environmental planning; Geography; Finance","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001433456,0.0005929235,0.0004296417,0.003985712,0.005349111,0.005326096,0.001269801,0.002256991,0.01861676],"category_scores_gemma":[0.006769052,0.0002727255,0.0006412969,0.002768558,0.00461474,0.007333313,0.008178742,0.003914719,0.002404932],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003808118,"about_ca_system_score_gemma":0.004055572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01703738,"about_ca_topic_score_gemma":0.02429958,"domain_scores_codex":[0.9970613,0.0006790297,0.0001853493,0.0003227796,0.0009149325,0.0008366415],"domain_scores_gemma":[0.9969172,0.0006051081,0.0007901941,0.0002248917,0.0008337664,0.0006288242],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00005119023,0.0002177638,0.07962859,0.0008816288,0.00007162323,0.002396014,0.03381894,0.0004389154,0.0004601273,0.2013876,0.2768887,0.4037589],"study_design_scores_gemma":[0.00001024187,0.0001531467,0.1175235,0.006712893,0.00005362998,0.002911902,0.09805442,0.0004445944,0.0007605566,0.03513427,0.738135,0.000105765],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1652191,0.02442776,0.002696884,0.1372352,0.00425058,0.0001684605,0.0009603723,0.00008154022,0.6649601],"genre_scores_gemma":[0.7394695,0.06061346,0.002221847,0.07082646,0.003342639,0.0003126147,0.001589123,0.0001998383,0.1214244],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01861676,"threshold_uncertainty_score":0.06227928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03462693578763933,"score_gpt":0.2883070328564917,"score_spread":0.2536800970688524,"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."}}