{"id":"W2110029939","doi":"10.3386/w10959","title":"Sorting It Out: International Trade and Protection With Heterogeneous Workers","year":2004,"lang":"en","type":"report","venue":"National Bureau of Economic Research","topic":"Employment and Welfare Studies","field":"Health Professions","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Sorting; Business; International trade; Computer science","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":[0.003446483,0.0002128813,0.0004204814,0.0005716023,0.0006134167,0.00002483996,0.000234553,0.000376692,0.0003441285],"category_scores_gemma":[0.00063095,0.0001839252,0.00008385577,0.00008802672,0.0002540376,0.0001237558,0.0002046759,0.001254689,0.00004569391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002816783,"about_ca_system_score_gemma":0.003895379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002406468,"about_ca_topic_score_gemma":0.002618742,"domain_scores_codex":[0.9966226,0.000216131,0.0008361284,0.0004861776,0.001390076,0.0004488357],"domain_scores_gemma":[0.9979458,0.0005382921,0.0005631965,0.0001562898,0.0007062651,0.00009016409],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003622914,0.0008678988,0.406345,0.008360639,0.009623574,0.0001129328,0.01732306,0.005002442,0.0003819076,0.1109438,0.4024884,0.03492746],"study_design_scores_gemma":[0.009313039,0.001810996,0.05635253,0.01100356,0.0002205996,0.000166435,0.007855536,0.0005095279,0.0002640862,0.2414484,0.6689144,0.002140871],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01671978,0.0008239286,0.00002796764,0.03541505,0.001969125,0.003256254,0.0001438301,0.00006843422,0.9415756],"genre_scores_gemma":[0.9791429,0.001208714,0.0001044059,0.00007222306,0.001921403,0.0006941824,0.0002404395,0.00006540967,0.01655035],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9624231,"threshold_uncertainty_score":0.750025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4881618911850027,"score_gpt":0.583005286821344,"score_spread":0.09484339563634125,"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."}}