{"id":"W4250423476","doi":"10.31230/osf.io/9b6yv","title":"Malidentifie : Comment résoudre le problème de la fraude des fruits de mer au Canada; enquête à Montréal","year":2019,"lang":"fr","type":"preprint","venue":"","topic":"Identification and Quantification in Food","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Humanities; Political science; Art","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":[0.008327273,0.0005901919,0.0006168328,0.002194064,0.009976013,0.006614444,0.003603207,0.003730508,0.01138747],"category_scores_gemma":[0.02938825,0.0005146852,0.0007050642,0.004437101,0.006614102,0.00352742,0.002810176,0.004722787,0.0009691475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04902448,"about_ca_system_score_gemma":0.09327929,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9870849,"about_ca_topic_score_gemma":0.9913941,"domain_scores_codex":[0.9894983,0.001561354,0.0004824694,0.001059221,0.004640881,0.002757831],"domain_scores_gemma":[0.9586217,0.006706939,0.004516567,0.001076403,0.02317008,0.005908352],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001781608,0.00009430959,0.4502605,0.001144298,0.0001580032,0.003447118,0.04247736,0.0005157717,0.001398438,0.02111772,0.3433361,0.1358721],"study_design_scores_gemma":[0.00004049806,0.0001498979,0.3928795,0.002183996,0.0001834732,0.002798058,0.0776207,0.001200078,0.001399513,0.002966851,0.5182702,0.0003072039],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.21101,0.0225178,0.003469263,0.68175,0.003896632,0.0002511333,0.004157367,0.0002621363,0.0726857],"genre_scores_gemma":[0.7471893,0.02131224,0.004868024,0.1584075,0.001453197,0.0001198679,0.001634638,0.0002312391,0.06478399],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04902448,"threshold_uncertainty_score":0.3556991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01250638335177583,"score_gpt":0.2515615153025543,"score_spread":0.2390551319507785,"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."}}