{"id":"W3107305927","doi":"10.1016/j.jue.2020.103300","title":"Commuting and innovation: Are closer inventors more productive?","year":2020,"lang":"en","type":"article","venue":"Journal of Urban Economics","topic":"Aviation Industry Analysis and Trends","field":"Economics, Econometrics and Finance","cited_by":54,"is_retracted":false,"has_abstract":false,"ca_institutions":"Bank of Canada","funders":"Wharton School, University of Pennsylvania; Harvard Business School","keywords":"Telecommuting; Productivity; Exploit; Identification (biology); Coronavirus disease 2019 (COVID-19); Labour economics; Demographic economics; Work (physics); Quality (philosophy); Construct (python library); Social distance; Economic geography; Economics; Business; Economic growth; Engineering; Computer science; Computer security","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":[0.002517554,0.0002034002,0.0007072712,0.001236113,0.0008579714,0.003629618,0.0007308954,0.0023016,0.02421851],"category_scores_gemma":[0.01594637,0.000182507,0.0005779832,0.001826651,0.002463172,0.004804933,0.0009008325,0.00122329,0.001032647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008060713,"about_ca_system_score_gemma":0.0008059434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006411165,"about_ca_topic_score_gemma":0.008270753,"domain_scores_codex":[0.9990475,0.0002648662,0.00006353578,0.000229293,0.0001340069,0.0002609564],"domain_scores_gemma":[0.9687506,0.01547457,0.008529616,0.001538753,0.001572057,0.004134344],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006886646,0.0008342415,0.8272042,0.0003344024,0.0003567826,0.0006120827,0.00466534,0.001206954,0.000732917,0.05187014,0.00465413,0.1068401],"study_design_scores_gemma":[0.0001114872,0.0003366972,0.8951579,0.0003172616,0.0003643789,0.0004820676,0.01762272,0.001258947,0.0003716374,0.07088213,0.01304754,0.00004717339],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.959251,0.007761945,0.001044775,0.009140142,0.0001483316,0.00002211988,0.0002966904,0.00001210129,0.0223229],"genre_scores_gemma":[0.9953838,0.001443498,0.0001092661,0.0003884146,0.0002419526,0.00000507488,0.0000516156,0.000005039201,0.002371241],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02421851,"threshold_uncertainty_score":0.08101892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0758971078806034,"score_gpt":0.2356465271270594,"score_spread":0.159749419246456,"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."}}