{"id":"W4388247850","doi":"10.30598/variancevol5iss2page147-158","title":"PENENTUAN PREMI MURNI DI KABUPATEN KEPAHIANG PROVINSI BENGKULU DENGAN MEMPERHITUNGKAN PELUANG KEJADIAN GEMPA BUMI DAN RASIO KERUSAKAN BANGUNAN","year":2023,"lang":"en","type":"article","venue":"VARIANCE Journal of Statistics and Its Applications","topic":"Geological and Geophysical Studies","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Actua","funders":"","keywords":"Seismology; Seismic hazard; Forensic engineering; Actuarial science; Geology; Engineering; Business","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003006811,0.0001928767,0.0003252394,0.0001005948,0.0005524169,0.0001197311,0.000290287,0.00007090848,0.0001332899],"category_scores_gemma":[0.0001533077,0.0001417199,0.00006395167,0.0004510408,0.0001153022,0.0001798276,0.00004358281,0.0002823291,0.0001871305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007902343,"about_ca_system_score_gemma":0.0000646105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002701984,"about_ca_topic_score_gemma":0.0005691902,"domain_scores_codex":[0.9985194,0.00006308035,0.0004511099,0.0002661049,0.0003189977,0.0003812891],"domain_scores_gemma":[0.9986422,0.0003559474,0.0002981351,0.0001499493,0.0002244246,0.0003293091],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001499185,0.0004522798,0.4739927,0.0004164703,0.0005170367,0.0002413652,0.003294119,0.003363389,0.003034217,0.06228347,0.007774204,0.4444809],"study_design_scores_gemma":[0.000297184,0.0002516668,0.9621423,0.00003441778,0.00007419111,0.00002531403,0.0003501988,0.005285794,0.00005994629,0.007365144,0.02389786,0.0002160153],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9819718,0.002605262,0.007025161,0.003131356,0.0003707058,0.0008813295,0.001749485,0.00009529135,0.00216961],"genre_scores_gemma":[0.9950124,0.001828585,0.002114135,0.000112645,0.0003030126,0.00001073912,0.0001350202,0.000007622239,0.0004758243],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4881496,"threshold_uncertainty_score":0.5779171,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01864418944053159,"score_gpt":0.2255622652436813,"score_spread":0.2069180758031497,"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."}}