{"id":"W6950611093","doi":"10.5683/sp3/8cwywn","title":"Replication Data and Code for: Firm heterogeneity, technology adoption and the spatial distribution of population: Theory and measurement","year":2022,"lang":"en","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bank of Canada","funders":"","keywords":"Replicate; Replication (statistics); Code (set theory); Distribution (mathematics); Spatial analysis; Data file","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":[0.005423544,0.002022789,0.00177101,0.004296476,0.001545547,0.003636989,0.004836028,0.002589272,0.2173978],"category_scores_gemma":[0.04174396,0.001793983,0.001983877,0.010417,0.0008416854,0.002315452,0.002874028,0.003105214,0.1486302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002840213,"about_ca_system_score_gemma":0.00555429,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07412405,"about_ca_topic_score_gemma":0.09880127,"domain_scores_codex":[0.9960311,0.0009095516,0.0006369866,0.00111926,0.0008448744,0.0004581567],"domain_scores_gemma":[0.979641,0.006019733,0.001632349,0.006670326,0.005119919,0.0009166225],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006194068,0.00001994692,0.001031001,0.0003472022,0.0000341365,0.00001229363,0.00004166253,0.0002376878,0.00004006264,0.0008854238,0.9957525,0.001536072],"study_design_scores_gemma":[0.001041402,0.00003016175,0.009313313,0.000419895,0.00008990341,0.00006076409,0.000196562,0.0005454242,0.0003489789,0.004644218,0.9832239,0.00008543079],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00009302995,0.00001560355,0.0001946627,0.00007221704,0.00002823837,0.00004480668,0.9986076,0.0002478843,0.000696077],"genre_scores_gemma":[0.0009847862,0.00002756553,0.001069174,0.00009960363,0.00001514054,0.0007541777,0.9947844,0.000311396,0.001953767],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2173978,"threshold_uncertainty_score":0.727268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05636388169085823,"score_gpt":0.315668197112447,"score_spread":0.2593043154215888,"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."}}