{"id":"W1553055087","doi":"10.1017/cbo9780511804861","title":"Decisions under Uncertainty: Probabilistic Analysis for Engineering Decisions","year":2005,"lang":"en","type":"book","venue":"","topic":"Diverse Scientific and Engineering Research","field":"Engineering","cited_by":87,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Probabilistic logic; Inference; Computer science; Statistical inference; Reliability (semiconductor); Principle of maximum entropy; Entropy (arrow of time); Econometrics; Management science; Mathematics; Artificial intelligence; Statistics; Engineering","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004314637,0.0005143418,0.0007082846,0.001462555,0.0001179366,0.0002058766,0.0006509775,0.0004285293,0.001284425],"category_scores_gemma":[0.0004608268,0.0004847268,0.0007030715,0.0008841497,0.00005690151,0.0001117413,0.0001210399,0.0004818091,0.0004426415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007679525,"about_ca_system_score_gemma":0.0001930372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004642607,"about_ca_topic_score_gemma":0.00007235527,"domain_scores_codex":[0.9974427,0.00000593719,0.0005265535,0.0005992936,0.0006626169,0.0007628725],"domain_scores_gemma":[0.9969701,0.001554142,0.00003254211,0.0008507411,0.0002162108,0.0003762813],"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":[0.000004178813,0.00001154276,4.410194e-7,0.00004166964,0.0008786336,0.00000306719,0.00002006655,0.8624849,0.00001143869,0.005830309,0.1258919,0.004821857],"study_design_scores_gemma":[0.0001461001,0.00001162312,0.00001857777,0.00008640202,0.0004503973,0.000001314758,0.0000115084,0.5664411,0.000002917629,0.0009512429,0.4314808,0.0003979987],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.00007299771,0.0005555326,0.7941005,0.00004252277,0.001188452,0.0008231815,0.000560406,0.001142567,0.2015138],"genre_scores_gemma":[0.004385388,0.0002944463,0.01684102,0.00002246006,0.0004633437,0.000196559,0.0008848279,0.0002080458,0.9767039],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.7772595,"threshold_uncertainty_score":0.9997604,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04196618323318055,"score_gpt":0.2634892373453814,"score_spread":0.2215230541122009,"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."}}