{"id":"W1605772580","doi":"10.22004/ag.econ.114484","title":"Heterogeneous Demand for Food Diversity: A Quantile Regression Analysis","year":2011,"lang":"en","type":"preprint","venue":"AgEcon Search (University of Minnesota, USA)","topic":"Organic Food and Agriculture","field":"Agricultural and Biological Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Quantile; Quantile regression; Diversity (politics); Econometrics; Distribution (mathematics); Economics; Welfare economics; Sample (material); Statistics; Geography; Demographic economics; Mathematics; Political science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003171147,0.0002971767,0.0006691978,0.00009461359,0.0006993814,0.0000383488,0.001035185,0.0004894156,0.001663598],"category_scores_gemma":[0.00001860842,0.0001477346,0.0008247267,0.0005483164,0.0001515944,0.0001427395,0.002297969,0.0003377241,0.00003308019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004841412,"about_ca_system_score_gemma":0.00002429047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001502542,"about_ca_topic_score_gemma":0.01595596,"domain_scores_codex":[0.9981338,0.0001438171,0.0001821721,0.0007415868,0.0003724156,0.0004262476],"domain_scores_gemma":[0.9988734,0.0001646753,0.0002816358,0.0001969123,0.0002608797,0.0002225688],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.004883354,0.00604105,0.1610666,0.003980765,0.03146164,0.0009713187,0.04157554,0.001302329,0.3042253,0.002556266,0.1066891,0.3352468],"study_design_scores_gemma":[0.003207138,0.00766642,0.8488058,0.000869308,0.008080431,0.00003539889,0.02101383,0.002087333,0.03304581,0.00294812,0.06815097,0.004089456],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962111,0.0003967455,0.00008245211,0.0009706789,0.0001178673,0.0005256861,0.001326778,0.00004810536,0.0003206258],"genre_scores_gemma":[0.9971665,0.0005447784,0.0003263088,0.00002892751,0.0001098238,0.000001039852,0.0004999153,0.000001861053,0.001320906],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6877392,"threshold_uncertainty_score":0.999249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0524463980077518,"score_gpt":0.2266242796130601,"score_spread":0.1741778816053083,"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."}}