{"id":"W247911794","doi":"","title":"Textual Analyses of Nutrition Messages on Prime Time Television.","year":2001,"lang":"en","type":"article","venue":"Canadian home economics journal","topic":"Culinary Culture and Tourism","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Prime time; Prime (order theory); Advertising; Business; Computer science; Psychology; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.0009193715,0.0001893636,0.0001576457,0.006031661,0.001030249,0.001213062,0.0002333342,0.000343935,0.01164652],"category_scores_gemma":[0.01246274,0.0000971392,0.00008484761,0.006351603,0.0006091508,0.0007888234,0.000487138,0.0004943318,0.001269621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001637519,"about_ca_system_score_gemma":0.0009799075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04668067,"about_ca_topic_score_gemma":0.0620952,"domain_scores_codex":[0.9991854,0.0003175249,0.00005565378,0.00006991708,0.0002863952,0.00008525367],"domain_scores_gemma":[0.9757296,0.01711719,0.002584303,0.000438736,0.003803226,0.0003269258],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.003732064,0.0007123129,0.1658437,0.005861796,0.0001364791,0.002412777,0.3729325,0.0004483444,0.05065303,0.01349586,0.1079147,0.2758565],"study_design_scores_gemma":[0.00005797177,0.0002307582,0.7217224,0.001004996,0.0001739434,0.0007314257,0.1162717,0.001421029,0.009532173,0.001173934,0.147622,0.00005762968],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9180502,0.001920457,0.0007749805,0.00170255,0.0002033571,0.0001837611,0.02077361,0.00006371859,0.05632745],"genre_scores_gemma":[0.9724571,0.001500962,0.001292364,0.0002668566,0.0002902536,0.0001619001,0.009163672,0.00007961972,0.01478729],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04668067,"threshold_uncertainty_score":0.09281796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02872744294437579,"score_gpt":0.2337261390369617,"score_spread":0.2049986960925859,"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."}}