{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02286715,0.0007011012,0.0006794435,0.002712336,0.001020696,0.003847415,0.001187507,0.0012237,0.001763501],"category_scores_gemma":[0.03320894,0.0003569906,0.0006710591,0.002118342,0.001369143,0.00323958,0.001752478,0.001274352,0.001135354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001377849,"about_ca_system_score_gemma":0.00352994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003667149,"about_ca_topic_score_gemma":0.005453588,"domain_scores_codex":[0.9804153,0.01374274,0.0008594718,0.001255782,0.003145887,0.0005807375],"domain_scores_gemma":[0.9679021,0.01546996,0.005518032,0.002561082,0.00735037,0.001198472],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001009396,0.0006954839,0.1920186,0.001519258,0.0002631412,0.0007501438,0.006999061,0.01600818,0.05215491,0.01266714,0.008557101,0.7073576],"study_design_scores_gemma":[0.0001950568,0.004513377,0.2661241,0.00741687,0.0006171997,0.002481941,0.03240144,0.3085068,0.1419261,0.09409346,0.1406542,0.00106943],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4935587,0.005388035,0.4688867,0.01137938,0.0006012849,0.0007508804,0.001274093,0.001272192,0.01688874],"genre_scores_gemma":[0.6599874,0.001477768,0.3350179,0.0008568895,0.00006848953,0.0001959499,0.0006625733,0.00009874041,0.001634205],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02286715,"threshold_uncertainty_score":0.1209345,"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."}}