{"id":"W7095067987","doi":"","title":"Application of Bayesian inference methods to inverse modeling for contaminant source identification","year":2003,"lang":"en","type":"article","venue":"","topic":"Diverse Scientific and Economic Studies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Identification (biology); Inverse method; Inverse; Inverse problem; Inference; Bayesian probability; Bayesian inference","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005753465,0.001015226,0.001162435,0.00228319,0.0006830902,0.00160142,0.001728057,0.001670587,0.002693581],"category_scores_gemma":[0.02334472,0.000983738,0.000945966,0.001915184,0.001827549,0.001777022,0.001854352,0.002433229,0.0007199377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001277913,"about_ca_system_score_gemma":0.001784671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006435013,"about_ca_topic_score_gemma":0.006475633,"domain_scores_codex":[0.9968218,0.001904704,0.0001101912,0.0002294968,0.0008707987,0.00006304869],"domain_scores_gemma":[0.9862288,0.01185406,0.0006553839,0.0004345643,0.0007425126,0.00008449711],"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":[0.00004311152,0.00005060717,0.001329387,0.0002974549,0.000176498,0.0001401475,0.0001663755,0.5680062,0.001507327,0.2792113,0.002796308,0.1462752],"study_design_scores_gemma":[0.00001678942,0.00001137023,0.0002493044,0.00004264033,0.00001596795,0.00005126525,0.0000150665,0.8233144,0.0003964821,0.1724888,0.003375051,0.00002296208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005308677,0.0004725316,0.9980806,0.0001835436,0.00001348708,0.000008246816,0.00002325683,0.0000494095,0.0006380883],"genre_scores_gemma":[0.1192722,0.00344857,0.8735844,0.0002227918,0.0003574618,0.0002245379,0.0002120904,0.0001434549,0.002534473],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006435013,"threshold_uncertainty_score":0.03042758,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07211553443150497,"score_gpt":0.2998523154095792,"score_spread":0.2277367809780743,"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."}}