{"id":"W1558522477","doi":"10.1111/j.1365-2966.2006.0547.x","title":"EXPLORING THE DETAILED LOCATION PATTERNS OF U.K. MANUFACTURING INDUSTRIES USING MICROGEOGRAPHIC DATA*","year":2008,"lang":"en","type":"article","venue":"Journal of Regional Science","topic":"Regional Economics and Spatial Analysis","field":"Economics, Econometrics and Finance","cited_by":262,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Economic and Social Research Council","keywords":"Stylized fact; Point (geometry); Set (abstract data type); Industrial organization; Business; Econometrics; Economic geography; Marketing; Computer science; Economics; Mathematics; Macroeconomics","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":[0.0003187189,0.0001456255,0.0002412638,0.0023556,0.000322058,0.0007736385,0.0002842769,0.0002317295,0.001903725],"category_scores_gemma":[0.003623525,0.0001829314,0.0002070452,0.006951231,0.0002234155,0.0005480747,0.0008990986,0.0002187564,0.0007769364],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007411286,"about_ca_system_score_gemma":0.0007417176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1725735,"about_ca_topic_score_gemma":0.2429353,"domain_scores_codex":[0.9996623,0.00006992436,0.00005428925,0.00009984368,0.00005895993,0.00005464261],"domain_scores_gemma":[0.9970395,0.0006419037,0.001163318,0.0004213848,0.0006429838,0.00009075453],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004946212,0.00001945629,0.9646919,0.0001031689,0.00008079965,0.0001329086,0.0008984609,0.007185874,0.001161952,0.001381038,0.002536432,0.02175839],"study_design_scores_gemma":[0.000005969717,0.0000279533,0.9821638,0.00003722242,0.00003408995,0.00009331212,0.002083065,0.00665539,0.0008943072,0.0004822377,0.007500278,0.0000225053],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9756575,0.0001830876,0.002658953,0.0001645413,0.000003422488,0.00002449748,0.01893828,0.0000386256,0.002331042],"genre_scores_gemma":[0.9882159,0.000204609,0.003871326,0.00001910842,0.000002864912,0.00002135046,0.007015456,0.000007170896,0.0006422913],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1725735,"threshold_uncertainty_score":0.3431381,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2532826901067979,"score_gpt":0.2572119393430412,"score_spread":0.003929249236243271,"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."}}