{"id":"W2174196974","doi":"10.1139/gen-2015-0033","title":"An economic analysis of private incentives to adopt DNA barcoding technology for fish species authentication in Canada","year":2015,"lang":"en","type":"article","venue":"Genome","topic":"Identification and Quantification in Food","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Incentive; Cheating; DNA barcoding; Business; Industrial organization; Barcode; Authentication (law); Enforcement; Biology; Public economics; Marketing; Natural resource economics; Fishery; Economics; Ecology; Computer security; Microeconomics; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001618296,0.00005926369,0.0001205322,0.0002505906,0.00002400596,0.00001192372,0.0002048118,0.00004324967,0.00001534557],"category_scores_gemma":[0.00005050134,0.00006817611,0.00003083381,0.0002679892,0.00002837064,0.00000446941,0.00003282161,0.00001889323,0.000002411241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001169463,"about_ca_system_score_gemma":0.0002433514,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005053697,"about_ca_topic_score_gemma":0.1849046,"domain_scores_codex":[0.9993568,0.0000187421,0.0002358547,0.0002256224,0.00004867595,0.0001143223],"domain_scores_gemma":[0.9994248,0.000004224059,0.00009771681,0.000329572,0.00008944872,0.00005428008],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002745975,0.00002856364,0.013931,0.000007477431,0.0001023329,8.219533e-8,0.000194644,0.002096063,0.9806355,0.002604801,0.0001615011,0.0002105469],"study_design_scores_gemma":[0.0004135908,0.0001090546,0.3671153,0.000004290181,0.0001028056,4.970758e-7,0.001707176,0.001550951,0.56479,0.0001168601,0.06385521,0.0002342853],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957067,0.00002763987,0.003197492,0.0005420205,0.00008995814,0.0001822648,0.0001713193,0.000004532168,0.00007807757],"genre_scores_gemma":[0.9984642,0.00001087944,0.0006750328,0.00006455919,0.0000243428,0.00004188046,0.0005071406,0.000006562691,0.0002054311],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4158456,"threshold_uncertainty_score":0.8299688,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02579937551837722,"score_gpt":0.2660139615840038,"score_spread":0.2402145860656265,"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."}}