{"id":"W1968877793","doi":"10.1007/s12076-013-0108-5","title":"Applying entropy econometrics to estimate data at a disaggregated spatial scale","year":2013,"lang":"en","type":"article","venue":"Letters in Spatial and Resource Sciences","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Econometrics; Proxy (statistics); Spatial analysis; Inference; Economics; Entropy (arrow of time); Personal income; Principle of maximum entropy; Statistics; Computer science; Mathematics","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":[0.00297558,0.0003813579,0.0008032738,0.002296976,0.0003677611,0.001287451,0.0004876823,0.000705334,0.001285053],"category_scores_gemma":[0.02231552,0.0005391085,0.0007776901,0.002066667,0.0008202122,0.002270019,0.00175678,0.001007065,0.0002069966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005394871,"about_ca_system_score_gemma":0.000493688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004302956,"about_ca_topic_score_gemma":0.003290373,"domain_scores_codex":[0.9990299,0.0005386445,0.00006679936,0.0001457668,0.0001603312,0.0000586519],"domain_scores_gemma":[0.9864196,0.01129592,0.0006745422,0.001077284,0.0004022268,0.000130427],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001092434,0.00007249625,0.03518211,0.0001050037,0.0004304666,0.0002890886,0.0002872581,0.7636173,0.003636079,0.1013279,0.000996041,0.09394704],"study_design_scores_gemma":[0.00000526191,0.000008110155,0.004569066,0.000005826092,0.0000120467,0.00002244721,0.00002881685,0.9448665,0.0003829578,0.04979964,0.0002894408,0.000009866797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1085817,0.0002115304,0.8892527,0.000289288,0.00003598151,0.00001966061,0.0002444096,0.0001882214,0.001176439],"genre_scores_gemma":[0.859382,0.0002785526,0.1383892,0.00009756524,0.0001390025,0.00005576585,0.0005942027,0.0000839974,0.0009796059],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004302956,"threshold_uncertainty_score":0.01573658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03900810868960812,"score_gpt":0.2387885370932035,"score_spread":0.1997804284035954,"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."}}