{"id":"W4246161596","doi":"10.31230/osf.io/v84rd","title":"Mislabelled: Montreal Investigation Results and How to Fix Canada's Seafood Fraud Problem","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Identification and Quantification in Food","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Traceability; Fish <Actinopterygii>; Business; Agency (philosophy); Labelling; Food standards; Wildlife; Food supply; Fishery; Environmental protection; Food safety; International trade; Geography; Agricultural economics; Engineering; Biology; Ecology; Economics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002532989,0.000251894,0.0002004402,0.00006932352,0.00008873759,0.0001598906,0.0002850236,0.0003480262,0.00001314371],"category_scores_gemma":[0.0001808055,0.0002526343,0.00004769431,0.0000794542,0.00004499417,0.000002828494,0.0003880365,0.0001909246,0.00001904252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004223651,"about_ca_system_score_gemma":0.0007590796,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.05766097,"about_ca_topic_score_gemma":0.3478458,"domain_scores_codex":[0.9982767,0.00008483351,0.0003034751,0.0008775003,0.000242694,0.0002148365],"domain_scores_gemma":[0.9983737,0.00001692457,0.0002262351,0.000874686,0.0003154175,0.0001929819],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001217489,0.0000427352,0.0006923648,0.000270575,0.0001651228,0.000001119137,0.000295609,0.0007903905,0.1117601,0.0004117917,0.8830201,0.002428305],"study_design_scores_gemma":[0.001567417,0.0002130798,0.02140162,0.0001302389,0.0000859283,0.00001067066,0.0003952803,0.0008533975,0.3935919,0.000309096,0.5801876,0.001253892],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9288138,0.0005384455,0.002076648,0.04579419,0.0013637,0.002636864,0.001103182,0.00007067429,0.01760253],"genre_scores_gemma":[0.9383313,0.00009771546,0.002173388,0.0009232647,0.000180413,0.0001135832,0.002894098,0.000031496,0.05525476],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3028326,"threshold_uncertainty_score":0.9999926,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01607496325155664,"score_gpt":0.2307080667259973,"score_spread":0.2146331034744407,"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."}}