{"id":"W4362561151","doi":"10.34260/jaebs.713","title":"The Determinants of Leather Exports of Pakistan: A Gravity Panel Approach","year":2023,"lang":"en","type":"article","venue":"Journal of Applied Economics and Business Studies","topic":"Global trade and economics","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Gravity model of trade; Tariff; Destinations; Gross domestic product; Product (mathematics); China; International economics; Exchange rate; Economics; Bilateral trade; Geographical distance; Geography; Business; Demographic economics; International trade; Demography; Monetary economics; Economic growth; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002766172,0.000209733,0.0001731427,0.0005604569,0.0002817865,0.0005245101,0.0001706897,0.0002251562,0.002600859],"category_scores_gemma":[0.0006082408,0.0001336168,0.0004892353,0.0007544406,0.0001759052,0.0003179556,0.0003669884,0.0004142496,0.0002820685],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003713138,"about_ca_system_score_gemma":0.0004842994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04362889,"about_ca_topic_score_gemma":0.02644489,"domain_scores_codex":[0.9998813,0.00003534325,0.000006022336,0.00002399065,0.00001824531,0.00003511684],"domain_scores_gemma":[0.9996151,0.0001386254,0.000102555,0.00003765834,0.00005363939,0.00005238144],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008932169,0.00009935092,0.9805179,0.00001987981,0.0001555504,0.0006290727,0.0003157983,0.009244694,0.0005484401,0.000960825,0.001470259,0.00594897],"study_design_scores_gemma":[0.000009918074,0.00007722132,0.9741426,0.00001583944,0.00009248662,0.0001290443,0.00100853,0.02197308,0.0002491209,0.0005411307,0.001748634,0.00001236441],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99767,0.00004404989,0.0003546737,0.0001551029,0.000003377076,0.000006049739,0.0007295737,0.000007148827,0.00103012],"genre_scores_gemma":[0.9984518,0.0000671191,0.0001568131,0.00001202273,0.000004812528,0.000003149511,0.0008694584,0.000001024358,0.0004338805],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04362889,"threshold_uncertainty_score":0.08674985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1161626400931143,"score_gpt":0.256614716839984,"score_spread":0.1404520767468698,"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."}}