{"id":"W4390041688","doi":"10.48550/arxiv.2312.11767","title":"Least-cost diets to teach optimization and consumer behavior, with applications to health equity, poverty measurement and international development","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Child Nutrition and Water Access","field":"Nursing","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada; Government of Canada","keywords":"Poverty; Maximization; Squash; Equity (law); Consumption (sociology); Workbook; Linear programming; Economics; Computer science; Econometrics; Supplemental Nutrition Assistance Program; Marketing; Environmental economics; Public economics; Agricultural economics; Microeconomics; Agriculture; Business; Economic growth; Accounting; Social science; Food security","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009068608,0.001285891,0.0005247801,0.0006547659,0.0004219647,0.001464001,0.0007075685,0.001141216,0.0375073],"category_scores_gemma":[0.004056896,0.0002893829,0.0007301317,0.001028709,0.001316508,0.002805624,0.001229229,0.003441687,0.007703688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001231059,"about_ca_system_score_gemma":0.0009922236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001397607,"about_ca_topic_score_gemma":0.00273126,"domain_scores_codex":[0.9997479,0.00009969657,0.00001610816,0.0000522791,0.00006078048,0.0000232393],"domain_scores_gemma":[0.9980434,0.001603729,0.00005269841,0.0001035855,0.0001012291,0.00009529018],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004596121,0.0005247131,0.001234983,0.0004903937,0.00003006241,0.0001421418,0.0007484645,0.00786531,0.001481337,0.4679399,0.2125315,0.3069652],"study_design_scores_gemma":[0.00003650758,0.00007809263,0.001218505,0.0002919968,0.00001288711,0.0002042167,0.0002796213,0.01488704,0.001199602,0.6210635,0.3607023,0.00002571494],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009711194,0.008114583,0.6791268,0.0356905,0.002167268,0.0002239075,0.0009183025,0.004682449,0.2593651],"genre_scores_gemma":[0.109255,0.01450571,0.712991,0.009624029,0.001878784,0.001052856,0.0009795802,0.002307778,0.1474053],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0375073,"threshold_uncertainty_score":0.1254744,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1835447231276597,"score_gpt":0.2813436290058817,"score_spread":0.09779890587822196,"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."}}