{"id":"W4410884911","doi":"10.1257/pandp.20251000","title":"Robot Hubs and the Use of Robotics in US Manufacturing Establishments","year":2025,"lang":"en","type":"article","venue":"AEA Papers and Proceedings","topic":"Firm Innovation and Growth","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Robotics; Robot; Artificial intelligence; Manufacturing engineering; Engineering; Manufacturing; Computer science; Future of robotics; Business; Marketing","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002284168,0.00007292036,0.0001897222,0.0001475704,0.00005411652,0.00009536444,0.00006087265,0.00004851485,0.00001112759],"category_scores_gemma":[0.00006965231,0.00006168232,0.0000193745,0.00019292,0.00008816295,0.000193488,0.00005244013,0.00008689459,0.000001186566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001263535,"about_ca_system_score_gemma":0.0000038878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002004174,"about_ca_topic_score_gemma":0.00001242035,"domain_scores_codex":[0.9994256,0.000001651408,0.0002834231,0.0001622342,0.00001855442,0.0001084911],"domain_scores_gemma":[0.9997655,0.00004153596,0.000110998,0.00004842892,0.00001707462,0.00001648208],"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":[0.00003859906,0.00001609214,0.4793196,0.00008265659,0.00002189532,2.69483e-7,0.0004368192,0.00004014253,0.00005028453,0.5177113,0.0003731737,0.001909097],"study_design_scores_gemma":[0.002292576,0.00002905402,0.9314291,0.00007554504,0.000009016248,0.000002192897,0.000179747,0.001886613,0.0006294924,0.02635141,0.03694135,0.0001739283],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9777872,0.0004767707,0.00003592311,0.002901388,0.00008742019,0.0001603311,0.000006438083,0.000008330434,0.01853622],"genre_scores_gemma":[0.99727,0.0004460413,0.0005810738,0.0008203086,0.000007866901,0.00000661516,0.000001394939,0.000004808779,0.0008619249],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4913599,"threshold_uncertainty_score":0.2515332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02789473180951468,"score_gpt":0.2022456420691182,"score_spread":0.1743509102596035,"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."}}