{"id":"W7095133748","doi":"","title":"Gravity Redux: Measuring International Trade Costs with Panel Data","year":2008,"lang":"en","type":"article","venue":"","topic":"Global trade and economics","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Gravity equation; Gravity model of trade; Panel data; Trade barrier; Bilateral trade; World trade","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gpt","categories":[],"domain":null,"study_design":"design_other","genre":"other","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"grok","categories":[],"domain":null,"study_design":"not_applicable","genre":"other","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"opus","categories":[],"domain":null,"study_design":"design_other","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002110464,0.000804261,0.0005527502,0.00752483,0.000397275,0.001669783,0.0008844167,0.000943648,0.008250732],"category_scores_gemma":[0.01361855,0.0004027904,0.0008162911,0.00970849,0.0003491571,0.00154886,0.001245761,0.0009279153,0.0017895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009310077,"about_ca_system_score_gemma":0.0006297139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01906724,"about_ca_topic_score_gemma":0.01132946,"domain_scores_codex":[0.9975536,0.001079231,0.000179544,0.0003980604,0.0006040196,0.0001855893],"domain_scores_gemma":[0.9925706,0.002807668,0.002300949,0.001436091,0.000678598,0.0002062015],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002335338,0.0003394646,0.5830967,0.0004896135,0.001412429,0.000262047,0.0006433445,0.07531991,0.001387193,0.04553208,0.1211102,0.1701736],"study_design_scores_gemma":[0.00009612173,0.0002053209,0.721157,0.0001167632,0.0002609421,0.0003715652,0.0007041446,0.1462142,0.003215184,0.04107016,0.08640262,0.0001860494],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4398423,0.001317039,0.1547596,0.001612789,0.0001690206,0.001139686,0.3361125,0.004334898,0.06071221],"genre_scores_gemma":[0.8027614,0.0004825382,0.09059476,0.0002657114,0.0001362144,0.001326531,0.09870537,0.0002705547,0.00545696],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01906724,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2512173514540444,"score_gpt":0.2305712990732699,"score_spread":0.02064605238077449,"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."}}