{"id":"W7101036938","doi":"","title":"NBER WORKING PAPER SERIES MEASURING CHANGES IN THE BILATERAL TECHNOLOGY GAPS BETWEEN","year":2015,"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":"Subject (documents); Developing country; Series (stratigraphy); Developed country","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":[0.008711413,0.0007525954,0.001695995,0.005898283,0.0007458804,0.003133081,0.001437141,0.001929579,0.01826985],"category_scores_gemma":[0.03652968,0.0004821923,0.0007791867,0.01130723,0.0005589046,0.002955202,0.001354534,0.002060764,0.009667983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004668945,"about_ca_system_score_gemma":0.007420691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08741878,"about_ca_topic_score_gemma":0.09590064,"domain_scores_codex":[0.9935022,0.0006723246,0.0009315938,0.0007789962,0.00362504,0.0004898144],"domain_scores_gemma":[0.9693913,0.007381064,0.01082702,0.00195008,0.008593695,0.001856931],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004948614,0.0002570623,0.1583034,0.001389599,0.0004967599,0.0002070424,0.0009206788,0.0040624,0.00119839,0.02908692,0.7091272,0.09445569],"study_design_scores_gemma":[0.000486944,0.0002103924,0.4892386,0.0004644445,0.0004634834,0.0002327069,0.001306354,0.003813722,0.003901203,0.01570717,0.4840207,0.0001543077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1039995,0.01348465,0.01488321,0.02426288,0.002077563,0.0007606251,0.7116683,0.001363519,0.1274998],"genre_scores_gemma":[0.341899,0.01802033,0.01238772,0.001918744,0.001706468,0.002241583,0.5271984,0.0007525159,0.0938753],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.08741878,"threshold_uncertainty_score":0.1738199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1817916775596993,"score_gpt":0.2187713078427611,"score_spread":0.03697963028306181,"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."}}