{"id":"W6963032582","doi":"10.17632/p45pfscm8n","title":"2015 Jakarta's Health Insurance Claim Data","year":2021,"lang":"en","type":"dataset","venue":"Mendeley Data","topic":"Security, Politics, and Digital Transformation","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Currency; Liberian dollar; National health insurance; Health insurance; Payment; General insurance","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002131605,0.000303689,0.0005229115,0.0001146049,0.0005913701,0.0005806079,0.005319838,0.0003245324,0.0004508507],"category_scores_gemma":[0.0008659599,0.0003279578,0.00004530838,0.0002423512,0.0002981628,0.002424614,0.001402107,0.000528613,0.0004943683],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002134287,"about_ca_system_score_gemma":0.002806837,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.0263454,"about_ca_topic_score_gemma":0.08527374,"domain_scores_codex":[0.9960324,0.0004304705,0.0006531654,0.0009813345,0.001092982,0.0008096472],"domain_scores_gemma":[0.9936552,0.0001857485,0.0002974168,0.005310554,0.0001304011,0.0004206901],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000004430556,0.0001224958,0.0000231584,0.0002150492,0.00003503326,0.0000103157,0.0004971095,2.025284e-7,3.248055e-8,0.0005968774,0.9957842,0.002711069],"study_design_scores_gemma":[0.0002206537,0.00002158284,0.00004550631,0.0001308652,0.00002259476,0.000003154613,0.00118519,0.00002032523,4.330183e-7,0.0005475553,0.9974681,0.0003340569],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000005577042,0.002906086,0.00005729773,0.003943831,0.001726461,0.000384349,0.9867672,0.00007647481,0.004132767],"genre_scores_gemma":[0.000121409,0.01371282,0.0001524678,0.002418238,0.001390276,0.000007803847,0.9818804,0.00001882926,0.0002977175],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05892834,"threshold_uncertainty_score":0.9999173,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.249525391307726,"score_gpt":0.4593355938405076,"score_spread":0.2098102025327817,"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."}}