{"id":"W4404650894","doi":"10.2139/ssrn.5002233","title":"Designing Surprise Bags for Surplus Foods","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Consumer Retail Behavior Studies","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Surprise; Business; Advertising; Marketing; Food science; Agricultural science; Psychology; Environmental science; Chemistry; Social psychology","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":[0.00100918,0.000783338,0.000496518,0.0003904834,0.0005998499,0.001664796,0.001165511,0.0008321659,0.01836498],"category_scores_gemma":[0.005280041,0.0005071677,0.0004732611,0.0002766494,0.0003492711,0.003086603,0.00191228,0.0008323717,0.004538951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003626312,"about_ca_system_score_gemma":0.0004176941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002726502,"about_ca_topic_score_gemma":0.0005662406,"domain_scores_codex":[0.9996188,0.0001252259,0.00002255283,0.00007752734,0.00008229118,0.00007358265],"domain_scores_gemma":[0.9984457,0.0007220788,0.0001672525,0.0002235338,0.0002272372,0.000214242],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.008560014,0.001529797,0.01391818,0.002090755,0.000153803,0.001738425,0.004528913,0.02625447,0.1526548,0.04228832,0.03537683,0.7109057],"study_design_scores_gemma":[0.0005996897,0.007108888,0.02226634,0.0007794763,0.0006685264,0.002665916,0.01146026,0.4818259,0.1602876,0.1078349,0.2041456,0.0003568131],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3642913,0.000618924,0.5912648,0.0007572122,0.0005937594,0.0006309957,0.0003802777,0.008112739,0.03335012],"genre_scores_gemma":[0.713464,0.0003424,0.2661175,0.0003991307,0.00009283288,0.0003915831,0.0005392408,0.001432297,0.01722107],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01836498,"threshold_uncertainty_score":0.06143701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02855795162156045,"score_gpt":0.272502132375103,"score_spread":0.2439441807535426,"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."}}