{"id":"W1656873868","doi":"10.1037/e667842007-001","title":"Could Behavioral Economics Help Improve Diet Quality for Nutrition Assistance Program Participants?","year":2007,"lang":"en","type":"dataset","venue":"PsycEXTRA Dataset","topic":"Decision-Making and Behavioral Economics","field":"Decision Sciences","cited_by":80,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Food and Nutrition Service; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; University of Alberta; Economic Research Service; Washington State University; U.S. Department of Agriculture","keywords":"Quality (philosophy); Behavioral economics; Supplemental Nutrition Assistance Program; Behavioural economics; Psychology; Gerontology; Public economics; Medicine; Economics; Food insecurity; Microeconomics; Biology; Food security; Ecology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.008452921,0.0009307653,0.001733756,0.0005977252,0.00046712,0.001888679,0.003522166,0.001136548,0.0009690105],"category_scores_gemma":[0.001518477,0.0008431762,0.0006445056,0.0005098152,0.0004399226,0.0009558532,0.0005015674,0.0009184869,0.001383951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003863921,"about_ca_system_score_gemma":0.000309562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004840982,"about_ca_topic_score_gemma":0.004999215,"domain_scores_codex":[0.9907454,0.0002641458,0.003666347,0.002655486,0.001180822,0.00148775],"domain_scores_gemma":[0.9898345,0.002123216,0.002331242,0.00457358,0.0004708561,0.0006665828],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009737664,0.002400152,0.00003938618,0.00006630723,0.00003172526,0.00001896293,0.000007510027,0.000003781311,0.00001135585,0.00001915873,0.8290642,0.1673636],"study_design_scores_gemma":[0.001422898,0.001136813,0.0001268952,0.00008429364,0.0002831966,0.000009270507,0.0001710155,0.00009028231,0.00003598778,0.00666659,0.9888934,0.001079283],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.009858391,0.0001769998,0.0002851295,0.0002349735,0.003843727,0.002658138,0.9828269,0.00009784666,0.00001783003],"genre_scores_gemma":[0.0003585264,0.0002600816,0.004488016,0.0005107458,0.0009360217,0.001130015,0.9921636,0.00006419789,0.00008878347],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1662844,"threshold_uncertainty_score":0.9999442,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3674282890381557,"score_gpt":0.5439569691639515,"score_spread":0.1765286801257958,"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."}}