{"id":"W1542449477","doi":"","title":"The Bright Side of MAUP: an Enquiry on the Determinants of Industrial Agglomeration in the United States","year":2008,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Regional Economics and Spatial Analysis","field":"Economics, Econometrics and Finance","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economies of agglomeration; Metropolitan area; Ranking (information retrieval); Economic geography; Sample (material); Econometrics; Quarter (Canadian coin); Economics; Geography; Zoning; Spatial econometrics; Regional science; Economic growth; Engineering; Computer 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002455911,0.0002792099,0.0003692509,0.0009716747,0.0005668228,0.001797792,0.0005437689,0.0004657351,0.001123297],"category_scores_gemma":[0.008058095,0.0001459768,0.0003099266,0.002064372,0.0007654196,0.00116278,0.001261278,0.0007167598,0.0001236482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006899757,"about_ca_system_score_gemma":0.000457063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02881558,"about_ca_topic_score_gemma":0.03896948,"domain_scores_codex":[0.9993671,0.0004125321,0.0000176631,0.00006481347,0.00009104573,0.00004690733],"domain_scores_gemma":[0.9970258,0.001892433,0.0003491367,0.0002677914,0.0003626775,0.0001022123],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002666446,0.000150918,0.6815796,0.000197516,0.0004179429,0.0004396608,0.005039811,0.01932188,0.001012916,0.1253288,0.0290432,0.1372012],"study_design_scores_gemma":[0.00004539933,0.0002617172,0.7665696,0.0003538515,0.0004733242,0.0002272271,0.01559242,0.0788741,0.002862444,0.08615682,0.04851216,0.00007102052],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9643511,0.005893669,0.01102223,0.01134782,0.00009420207,0.00001457757,0.0007572548,0.00005608671,0.006463143],"genre_scores_gemma":[0.9910978,0.002291249,0.003999976,0.0008071976,0.0001075752,0.00002499276,0.0005201399,0.0000236029,0.001127491],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02881558,"threshold_uncertainty_score":0.05729568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1158053869016785,"score_gpt":0.3102218591773516,"score_spread":0.1944164722756731,"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."}}