{"id":"W4362663514","doi":"10.2166/ws.2023.085","title":"Mobile DMA testing for leakage assessment: perspectives from Ontario, Canada","year":2023,"lang":"en","type":"article","venue":"Water Science & Technology Water Supply","topic":"Water Systems and Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; HydraTek (Canada)","funders":"","keywords":"Leakage (economics); Software deployment; Computer science; Context (archaeology); Reliability engineering; Environmental science; Engineering; Geography; Operating system","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002834263,0.0007758437,0.0005652366,0.001604494,0.003652237,0.003789209,0.001896806,0.000897197,0.004772128],"category_scores_gemma":[0.003537953,0.0003383642,0.0004563522,0.002768151,0.00201206,0.001078148,0.001381429,0.0009224921,0.0004797175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.06850527,"about_ca_system_score_gemma":0.1263346,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9909762,"about_ca_topic_score_gemma":0.9965138,"domain_scores_codex":[0.996181,0.0004681755,0.0001208925,0.0002592958,0.002162733,0.0008078967],"domain_scores_gemma":[0.9923287,0.0005313896,0.0002920067,0.0001509061,0.006174646,0.0005224179],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001666881,0.0006156841,0.229412,0.004297486,0.000244739,0.003915585,0.01052622,0.0188438,0.06555484,0.03333383,0.08280008,0.5487889],"study_design_scores_gemma":[0.0002048323,0.0015296,0.2817146,0.002815642,0.0003456787,0.00132615,0.03026422,0.01511005,0.02906333,0.00430117,0.6329045,0.0004203123],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5209098,0.05712983,0.0351116,0.05630194,0.0008709294,0.001430155,0.01001167,0.0009955022,0.3172386],"genre_scores_gemma":[0.9067696,0.02300654,0.02940746,0.003323528,0.0001006738,0.0002059895,0.001604665,0.0001298756,0.03545169],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06850527,"threshold_uncertainty_score":0.4970429,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008016543288366979,"score_gpt":0.2054967805156461,"score_spread":0.1974802372272791,"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."}}