{"id":"W2329618392","doi":"10.1061/41036(342)47","title":"Comparative Evaluation of Two Algorithms for Locating Contaminant Ingress Points","year":2009,"lang":"en","type":"article","venue":"World Environmental and Water Resources Congress 2009","topic":"Water Systems and Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Node (physics); Event (particle physics); Algorithm; Computer science; Identification (biology); Metric (unit); False alarm; Hazard; Data mining; Stage (stratigraphy); ALARM; Real-time computing; Engineering; Artificial intelligence; Geology; Structural 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":[],"consensus_categories":[],"category_scores_codex":[0.0002644648,0.000160915,0.0002461261,0.00008733758,0.00007633481,0.00003288637,0.00007236611,0.00003160728,0.00003580701],"category_scores_gemma":[0.000001152974,0.0001209441,0.00003616876,0.00003342089,0.00005912017,0.000126741,0.00002003961,0.00005352237,0.000005840366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000495284,"about_ca_system_score_gemma":0.000001171244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001238303,"about_ca_topic_score_gemma":0.0000447214,"domain_scores_codex":[0.9990834,0.00004398786,0.0002856833,0.0001732027,0.0002121201,0.0002016174],"domain_scores_gemma":[0.9997508,0.00001622444,0.00005405172,0.0001118354,0.00001482397,0.0000522609],"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":[0.0002241958,0.0003885948,0.01856502,0.0003304851,0.0003882575,0.000009662989,0.03348919,0.7331114,0.07001323,0.0001616808,0.002544865,0.1407734],"study_design_scores_gemma":[0.003412669,0.0001978096,0.01684988,0.0001993512,0.0001398541,0.0000111864,0.0004950562,0.7780746,0.1923428,0.000459391,0.007347483,0.0004699789],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955491,0.0009926113,0.001565046,0.00004387137,0.0001648438,0.0005509793,0.00003547944,0.00003879713,0.001059309],"genre_scores_gemma":[0.9983589,0.00001503006,0.000745972,0.00002366561,0.00006357635,0.00003220239,0.00006583606,0.00001332582,0.0006814772],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1403034,"threshold_uncertainty_score":0.4931957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0210416460995384,"score_gpt":0.2448421638082529,"score_spread":0.2238005177087145,"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."}}