{"id":"W2336085486","doi":"10.5539/mas.v10n6p58","title":"Comparing Monitoring and Inspection Systems in Iran and America","year":2016,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Economic, financial, and policy analysis","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Conceptualization; Language change; Identification (biology); Strengths and weaknesses; Institution; Islamic republic; Process management; Business; Management system; Computer science; Financial institution; Risk analysis (engineering); Islam; Operations management; Political science; Finance; Engineering; Psychology; Geography; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004395178,0.00009094915,0.0002699519,0.0003469782,0.0001874923,0.0001193052,0.0001407916,0.00003478816,0.000002357035],"category_scores_gemma":[0.00002209729,0.00008770267,0.00001549926,0.0002859693,0.0002828104,0.0002783992,0.00007150575,0.00005700474,0.00004014176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001257801,"about_ca_system_score_gemma":0.000009679131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001053925,"about_ca_topic_score_gemma":0.00005085172,"domain_scores_codex":[0.9989752,0.000003563168,0.0003006326,0.00043775,0.00002811613,0.0002547338],"domain_scores_gemma":[0.9995788,0.00002993841,0.0001401715,0.0001720074,0.000006025711,0.00007302205],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001233162,0.00002315008,0.8533287,0.00002122724,0.000007977558,7.362598e-7,0.001905667,0.0003315212,0.02051732,0.11453,0.000009428491,0.009311943],"study_design_scores_gemma":[0.0008887991,0.00002976198,0.8156611,0.00004930494,0.000004927757,0.000004790179,0.0002917324,0.1460762,0.001036157,0.03381792,0.001664648,0.0004746984],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9723677,0.000680448,0.01545851,0.0001014546,0.0001352687,0.0000888053,0.000005986696,0.00002742273,0.01113439],"genre_scores_gemma":[0.9993618,0.0002900638,0.0001669724,0.00001903218,0.00007209805,0.0000183367,1.636103e-7,0.000006926958,0.0000646302],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1457446,"threshold_uncertainty_score":0.3576411,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05396124109144876,"score_gpt":0.2310574436445096,"score_spread":0.1770962025530609,"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."}}