{"id":"W2830823864","doi":"10.4081/ijfs.2018.6894","title":"DNA barcoding for the verification of supplier’s compliance in the seafood chain: How the lab can support companies in ensuring traceability","year":2018,"lang":"en","type":"article","venue":"Italian Journal of Food Safety","topic":"Identification and Quantification in Food","field":"Biochemistry, Genetics and Molecular Biology","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Grieg Seafood (Canada)","funders":"","keywords":"Traceability; Barcode; DNA barcoding; Identification (biology); Business; Sample (material); Supply chain; Certification; Protocol (science); Authentication (law); Decision tree; Computer science; Computational biology; Biotechnology; Biology; Marketing; Evolutionary biology; Data mining; Computer security; Ecology; Medicine; Chemistry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002144114,0.0001040537,0.000157904,0.00004867458,0.000192808,0.00004807797,0.0006040811,0.00006037607,0.000009842574],"category_scores_gemma":[0.0002384931,0.00006192362,0.0001162626,0.0001865372,0.0002995708,0.00001263259,0.00002605144,0.0001457232,8.398392e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002699577,"about_ca_system_score_gemma":0.00009302422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005385685,"about_ca_topic_score_gemma":0.0003172228,"domain_scores_codex":[0.998738,0.0002061552,0.000526045,0.000163108,0.0001988634,0.000167808],"domain_scores_gemma":[0.9986408,0.00013037,0.0004188967,0.0004673363,0.0003150362,0.00002761174],"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":[0.003070197,0.001468717,0.03779817,0.0005637861,0.0006987708,0.000003895107,0.04098779,0.0007698311,0.8583925,0.02443568,0.01285837,0.0189523],"study_design_scores_gemma":[0.002702229,0.001728807,0.424575,0.0001276972,0.0000817866,0.0001214786,0.02233559,0.0008256975,0.4790246,0.0006964165,0.06741976,0.0003609556],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9781665,0.0002803807,0.002141183,0.0181541,0.0003467353,0.0005937047,0.0001149078,0.000003250633,0.0001992526],"genre_scores_gemma":[0.9993653,0.00004642493,0.0001551244,0.0001455032,0.0001736225,0.00002114644,0.00002243242,0.000009394802,0.00006100202],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3867768,"threshold_uncertainty_score":0.2525172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06938377526082601,"score_gpt":0.2999913970580302,"score_spread":0.2306076217972042,"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."}}