{"id":"W3123578918","doi":"10.24149/gwp215","title":"Geographic Barriers to Commodity Price Integration: Evidence from US Cities and Swedish Towns, 1732-1860","year":2014,"lang":"en","type":"preprint","venue":"Federal Reserve Bank of Dallas, Globalization and Monetary Policy Institute Working Papers","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Social Sciences and Humanities Research Council of Canada; National Science Foundation","keywords":"Commodity; Convergence (economics); Dispersion (optics); Price dispersion; Economics; Economic geography; International economics; Geography; Econometrics; Macroeconomics; Market economy","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007373795,0.0006657494,0.001250439,0.0006693688,0.0005219961,0.0005674511,0.0006718419,0.0004863455,0.0002069185],"category_scores_gemma":[0.001328925,0.0007762792,0.0002525437,0.0003584931,0.0004247268,0.0004926258,0.0007696542,0.0005027304,0.00001775678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002622849,"about_ca_system_score_gemma":0.0001720131,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08071121,"about_ca_topic_score_gemma":0.008246027,"domain_scores_codex":[0.9964673,0.0001242681,0.001420425,0.001178622,0.0001628071,0.0006466048],"domain_scores_gemma":[0.9972599,0.0001522971,0.0009450465,0.0008516041,0.00004991008,0.0007412644],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005114507,0.00007105769,0.8239425,0.0006539642,0.0009477339,0.00001264745,0.003687875,0.110776,0.00003914117,0.04628785,0.008392641,0.00467717],"study_design_scores_gemma":[0.002132501,0.0004269979,0.6464446,0.003405298,0.0001633718,0.00002431965,0.0003534912,0.0607149,0.00006806802,0.03394505,0.2495608,0.002760556],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9656262,0.007958586,0.001839854,0.005702145,0.001413217,0.000879924,0.001692124,0.000107671,0.01478026],"genre_scores_gemma":[0.9842495,0.008641121,0.001480459,0.003699404,0.000717512,0.00004464172,0.0008499909,0.00004311365,0.0002742955],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2411682,"threshold_uncertainty_score":0.9994688,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06541876951880357,"score_gpt":0.2646420536245353,"score_spread":0.1992232841057317,"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."}}