{"id":"W4412585977","doi":"10.1007/s42973-025-00211-x","title":"The heterogeneous determinants of coagglomeration: a differenced perspective","year":2025,"lang":"en","type":"article","venue":"Japanese Economic Review","topic":"Regional Economics and Spatial Analysis","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Perspective (graphical); Regional science; Economic geography; Economics; Geography; Computer science; Artificial intelligence","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":[0.0017879,0.0001674484,0.0006596482,0.002745511,0.0007284304,0.002342008,0.0005974392,0.0006108158,0.004457824],"category_scores_gemma":[0.006450018,0.0001291365,0.0005894593,0.003690032,0.002580154,0.002100766,0.001208073,0.0008734351,0.0001794516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001812006,"about_ca_system_score_gemma":0.00104763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01154747,"about_ca_topic_score_gemma":0.01033345,"domain_scores_codex":[0.9989715,0.0003939641,0.00004574295,0.0001995948,0.0001679467,0.0002213083],"domain_scores_gemma":[0.9948579,0.002520567,0.0008639613,0.0004443675,0.0007451407,0.0005680521],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0002317001,0.0001898138,0.1993225,0.0004344472,0.0005123229,0.0005582497,0.002582979,0.003668248,0.0005878159,0.7082836,0.004501598,0.07912679],"study_design_scores_gemma":[0.0000378577,0.0001468694,0.5472784,0.0002193652,0.0007031969,0.000578897,0.008577925,0.009142651,0.0004721705,0.3883143,0.0444617,0.00006662018],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8345709,0.05175177,0.01713074,0.01492665,0.0003428079,0.00006593617,0.0008076908,0.0000244968,0.08037902],"genre_scores_gemma":[0.9913693,0.005994004,0.000527427,0.0002292069,0.0001720968,0.000008147686,0.00009198268,0.000005392538,0.001602579],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01154747,"threshold_uncertainty_score":0.02296054,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.026382387834822,"score_gpt":0.2722393780985458,"score_spread":0.2458569902637238,"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."}}