{"id":"W3196737769","doi":"10.1111/twec.13190","title":"Decomposition of extensive and intensive margin impacts of trade policies","year":2021,"lang":"en","type":"article","venue":"World Economy","topic":"Global trade and economics","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Food and Agriculture","keywords":"Economics; Margin (machine learning); Decomposition; International economics; International trade; Macroeconomics; Econometrics; Computer science; Chemistry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001244145,0.0001337881,0.0005615002,0.0002260797,0.00003290928,0.00002813935,0.00009551734,0.00005486634,0.0002428501],"category_scores_gemma":[0.0000490533,0.000175489,0.0001250308,0.0001695053,0.0001086341,0.0002326359,0.00006082643,0.00008263636,0.00003970933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005899365,"about_ca_system_score_gemma":0.00002590015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002234042,"about_ca_topic_score_gemma":0.0001943807,"domain_scores_codex":[0.9988056,0.000009077139,0.0006761307,0.0002798826,0.0000101556,0.0002191938],"domain_scores_gemma":[0.9991311,0.00006354836,0.0004158527,0.0002421713,0.00005812913,0.00008922953],"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":[0.000047039,0.0001245512,0.2453026,0.0002073367,0.0002811732,0.00001221659,0.001350402,0.0001471414,0.0008064138,0.7468931,0.004047669,0.0007803303],"study_design_scores_gemma":[0.002311917,0.0001927713,0.6888092,0.0001618326,0.00005265763,0.0001155087,0.002554613,0.001143736,0.03661314,0.1713097,0.09591701,0.0008179408],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9328935,0.003508858,0.00007922353,0.002741082,0.0001811397,0.0001177999,0.0001991237,0.00001109183,0.06026817],"genre_scores_gemma":[0.9975048,0.0003888694,0.0007256932,0.001090868,0.000046667,0.00000469407,0.00002451349,0.00001404281,0.0001998755],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5755834,"threshold_uncertainty_score":0.7156235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03341865696777263,"score_gpt":0.2328114784401431,"score_spread":0.1993928214723704,"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."}}