{"id":"W2757115851","doi":"10.1007/978-3-319-59928-1_11","title":"Uncertainty Analysis by Bayesian Inference","year":2017,"lang":"en","type":"book-chapter","venue":"Ecological Informatics","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"The Scarborough Hospital","funders":"","keywords":"Credibility; Bayesian probability; Computer science; Bayesian inference; Consistency (knowledge bases); Inference; Process (computing); Statistical inference; Frequentist inference; Machine learning; Calibration; Adaptive management; Uncertainty analysis; Management science; Data mining; Risk analysis (engineering); Artificial intelligence; Environmental resource management; Mathematics; Engineering; Statistics","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002885364,0.0003189672,0.0005477138,0.00006959269,0.0004639711,0.00006769273,0.0006476284,0.0004386063,0.01687276],"category_scores_gemma":[0.00008062566,0.0002351156,0.000212003,0.00002981058,0.0006976575,0.0002006787,0.0009572717,0.0003867075,0.003360881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001372711,"about_ca_system_score_gemma":0.000005364107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003187713,"about_ca_topic_score_gemma":0.000277711,"domain_scores_codex":[0.9986813,0.00001361147,0.0004920712,0.0002058238,0.0002657821,0.0003413945],"domain_scores_gemma":[0.9987879,0.0001117488,0.0004665327,0.0005164256,0.000009157196,0.0001081994],"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":[0.00008225389,0.000331566,0.05944417,0.000180441,0.00742891,0.0001369541,0.002471298,0.06747285,0.000002748074,0.05001878,0.7852859,0.02714406],"study_design_scores_gemma":[0.000192801,0.000175503,0.004504465,0.00001031134,0.0008741902,8.090217e-7,0.0000257002,0.008828131,0.000001250348,0.02527911,0.9595264,0.0005813026],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0005372263,0.00001441732,0.002102856,0.0003362251,0.00009481567,0.0002255104,0.00005119455,0.00006590263,0.9965718],"genre_scores_gemma":[0.2093064,0.0004764616,0.0007622833,0.00209575,0.00003459477,0.00002895334,0.0003079341,0.00001380709,0.7869738],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.209598,"threshold_uncertainty_score":0.9974151,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01687022773114049,"score_gpt":0.2429275560015178,"score_spread":0.2260573282703773,"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."}}