{"id":"W4393642529","doi":"10.5281/zenodo.6350793","title":"Fault-based probabilistic seismic hazard analysis in regions with low strain rates and a thick seismogenic layer: a case study from Malawi. Supplementary Files","year":2022,"lang":"en","type":"dataset","venue":"Explore Bristol Research","topic":"Infrastructure Resilience and Vulnerability Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Research Councils UK","keywords":"Seismology; Geology; Fault (geology); Probabilistic logic; Seismic hazard; Layer (electronics); Hazard; Computer science; Materials science; Artificial intelligence; Composite material","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":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001923835,0.0006679802,0.001230925,0.002404506,0.0005909523,0.0002945665,0.000850138,0.0002754863,0.008477595],"category_scores_gemma":[0.0002498445,0.0006136438,0.0002614531,0.004175098,0.0004669029,0.0001756852,0.00035958,0.002919908,0.00001046101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008200944,"about_ca_system_score_gemma":0.0005117137,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.04445497,"about_ca_topic_score_gemma":0.1481098,"domain_scores_codex":[0.9935942,0.001581186,0.0008353541,0.001355411,0.001608572,0.001025307],"domain_scores_gemma":[0.9964729,0.001228752,0.0001170728,0.001685574,0.0001972948,0.0002984121],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002162354,0.0006068241,0.005996172,0.0005819972,0.00236699,0.01453734,0.001743021,0.5241576,0.00001436161,0.000001216576,0.4493371,0.0004411234],"study_design_scores_gemma":[0.007234499,0.003482496,0.005221929,0.000575121,0.008094404,0.0007383791,0.1623248,0.5911834,0.00009275119,0.0008982483,0.2155533,0.004600714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.3666063,0.0003128547,0.0002026203,0.0001630704,0.00003108949,0.001263983,0.631372,0.00004576761,0.000002235045],"genre_scores_gemma":[0.457384,0.0001139738,0.0001126472,0.00003571784,0.00005539809,0.001071675,0.5411618,0.00005266506,0.00001219019],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2337838,"threshold_uncertainty_score":0.9996315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05025332767367227,"score_gpt":0.3402237917922263,"score_spread":0.2899704641185541,"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."}}