{"id":"W3114959829","doi":"10.2139/ssrn.3707438","title":"Consumption Variety in Food Recommendation","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Digital Marketing and Social Media","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; Université du Québec à Montréal; McGill University","funders":"","keywords":"Variety (cybernetics); Consumption (sociology); Business; Marketing; Computer science; Sociology; Artificial intelligence; Social science","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":[0.001655606,0.0003199394,0.0004769235,0.002014515,0.0007985073,0.002921966,0.0004037333,0.001107511,0.01965203],"category_scores_gemma":[0.01187498,0.0004338438,0.0008345569,0.001576151,0.0006671526,0.002856866,0.0008549034,0.001326285,0.00138945],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007529642,"about_ca_system_score_gemma":0.0002276959,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007622599,"about_ca_topic_score_gemma":0.01044181,"domain_scores_codex":[0.9991993,0.0003273053,0.00004716993,0.0002171537,0.0001327833,0.00007626572],"domain_scores_gemma":[0.9879079,0.008709941,0.0009585048,0.0006237598,0.0008208054,0.0009789848],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002231548,0.0006675201,0.9504188,0.0001172775,0.0004803583,0.0003335752,0.0009159694,0.001952164,0.001363819,0.005390111,0.001621614,0.0345072],"study_design_scores_gemma":[0.00005085461,0.0003961104,0.9823973,0.00005389035,0.0002457269,0.0002075538,0.001909165,0.008366745,0.0003545033,0.004404046,0.001576694,0.00003737346],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.97434,0.001313111,0.001073388,0.0005567017,0.00005886764,0.00002659364,0.0004363786,0.00003641099,0.02215854],"genre_scores_gemma":[0.9974725,0.0001214288,0.0003013137,0.00005005284,0.00004230497,0.000007013433,0.0001982372,0.00001260802,0.00179451],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01965203,"threshold_uncertainty_score":0.06574261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03081676021664382,"score_gpt":0.2901587404425018,"score_spread":0.259341980225858,"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."}}