{"id":"W2297878978","doi":"10.1007/s11269-016-1241-x","title":"Prediction of Timing of Watermain Failure Using Gene Expression Models","year":2016,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Water Systems and Optimization","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Gene expression programming; Parametric statistics; Environmental science; Reliability engineering; Structural engineering; Engineering; Computer science; Statistics; Mathematics","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":[0.0001241639,0.0001058003,0.0001422713,0.0001397131,0.0000262182,0.00001110127,0.0001072643,0.00004495825,0.00002522715],"category_scores_gemma":[4.081928e-7,0.00005997516,0.00004047594,0.00004361897,0.00001659351,0.0001829631,0.0000844665,0.00002037752,0.00000302335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003376734,"about_ca_system_score_gemma":4.433577e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001265194,"about_ca_topic_score_gemma":0.000001686936,"domain_scores_codex":[0.9992062,0.0000222781,0.0003090597,0.0001273414,0.000169918,0.0001652096],"domain_scores_gemma":[0.9996914,0.000003040371,0.00004433881,0.0002117632,0.00002304933,0.00002635828],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000009703171,0.00001117575,0.0001835808,0.0003468259,0.00004970616,0.000002312954,0.001888971,0.6292974,0.3673885,0.00001835993,0.0002929343,0.0005104879],"study_design_scores_gemma":[0.0004161133,0.00001917837,0.00008057906,0.0004276982,0.00003121139,0.000001675378,0.00009748283,0.1984089,0.7978097,0.0001272994,0.002478932,0.0001012332],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6745272,0.00003097187,0.3237168,0.00001391577,0.0001071445,0.0001828479,0.00001229879,0.00007612038,0.001332746],"genre_scores_gemma":[0.9923654,0.0000165097,0.006988738,0.000001710029,0.00004380854,0.000009904959,0.000008955725,0.0000247415,0.0005402551],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4308885,"threshold_uncertainty_score":0.2445716,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02261287429235681,"score_gpt":0.1788409072901465,"score_spread":0.1562280329977896,"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."}}