{"id":"W7053050256","doi":"","title":"Understanding international price differences uing barcode data","year":2008,"lang":"en","type":"article","venue":"","topic":"Magneto-Optical Properties and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Stylized fact; Barcode; Purchasing power parity; Replicate; Law of one price; Market segmentation; Price setting; Database transaction; Price level","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":[0.00002626305,0.00005468242,0.00005113148,0.00002115323,0.00007416748,0.0000242612,0.0003176678,0.00002008493,0.0008436912],"category_scores_gemma":[0.00001447489,0.000043578,0.000009880086,0.00005280754,0.0000302676,0.0001387288,0.00009588951,0.00006594825,0.00005843072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003415807,"about_ca_system_score_gemma":0.000004836231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001335548,"about_ca_topic_score_gemma":0.000006397383,"domain_scores_codex":[0.9995887,0.000002684572,0.00009589201,0.0001099937,0.00009667218,0.0001060064],"domain_scores_gemma":[0.9996989,0.00003408882,0.000006080767,0.0002124055,0.000007507061,0.00004098829],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001983181,0.0002072153,0.01485343,0.0001666103,0.0003273046,0.00002978499,0.001425785,0.02459498,0.01873457,0.7503556,0.1766138,0.01267107],"study_design_scores_gemma":[0.0001024029,0.000006732682,0.0020747,0.000008590781,0.000004213112,0.000009413902,0.0001861652,0.9607245,0.0001382517,0.001094882,0.03550944,0.0001407451],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03202244,0.00008811313,0.5679256,0.0007898824,0.0001821272,0.00007713655,0.00001663335,0.0003318287,0.3985662],"genre_scores_gemma":[0.9945274,0.0001665145,0.003655698,0.00004912336,0.00007796696,0.00000468594,0.00001922918,0.000007655342,0.001491712],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.962505,"threshold_uncertainty_score":0.9237827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3248994878449077,"score_gpt":0.2553806083269729,"score_spread":0.06951887951793478,"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."}}