{"id":"W4390097035","doi":"10.1016/j.foodpol.2023.102587","title":"The food and beverage marketing monitoring framework for Canada: Development, implementation, and gaps","year":2023,"lang":"en","type":"article","venue":"Food Policy","topic":"Obesity, Physical Activity, Diet","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; Health Canada","keywords":"Marketing; Business; Food marketing; Social marketing; Scope (computer science); Population; Consumption (sociology); Product (mathematics); Environmental health; Medicine; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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":[0.0002475228,0.0001096152,0.0001404046,0.0000506108,0.000386452,0.00004127301,0.0000524189,0.00003637754,0.00000222189],"category_scores_gemma":[0.0003939733,0.00008868974,0.00001924858,0.0002254299,0.00003003552,0.00004711387,0.0001123517,0.000117095,0.000001185548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001146028,"about_ca_system_score_gemma":0.0003674045,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.008229012,"about_ca_topic_score_gemma":0.03666913,"domain_scores_codex":[0.9991452,0.00003049531,0.0001463603,0.0001737057,0.0001684526,0.0003358317],"domain_scores_gemma":[0.9989336,0.0007220985,0.0000538296,0.0001192433,0.00004541418,0.0001258406],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001207054,0.0002005579,0.8534346,0.0007476375,0.0005947216,0.000008411695,0.002385164,0.000001139437,0.0005411985,0.02716454,0.007738896,0.1070624],"study_design_scores_gemma":[0.0004752083,0.00015496,0.9785252,0.00005876514,0.00002056029,0.000003354999,0.0007764677,0.000005984542,0.003991228,0.005492893,0.01039376,0.0001015966],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955594,0.0001311035,0.00001922289,0.003562291,0.0001140973,0.0003651307,0.00003541019,0.00004428286,0.0001690498],"genre_scores_gemma":[0.9980552,0.00005385521,0.0007609668,0.0001584886,0.0007372142,0.00007466573,0.00001213678,0.00002220378,0.0001252645],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1250906,"threshold_uncertainty_score":0.9983753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02612676823191922,"score_gpt":0.32812050762794,"score_spread":0.3019937393960208,"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."}}