{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002761407,0.0002201039,0.0002322777,0.000567656,0.0003965295,0.0001447171,0.0006492482,0.0001288324,0.00006796655],"category_scores_gemma":[0.00001223657,0.0001489835,0.00003241517,0.0007135083,0.0002228642,0.0004032924,0.0002140823,0.0001995041,0.00004671936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006432954,"about_ca_system_score_gemma":0.000191492,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.3152114,"about_ca_topic_score_gemma":0.6097956,"domain_scores_codex":[0.9980462,0.000008062015,0.0002716147,0.000518856,0.0002609122,0.0008943503],"domain_scores_gemma":[0.9993113,0.00001729143,0.0000195081,0.0004248453,0.0001512341,0.00007582077],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000008859013,0.00004546018,0.1065333,0.00009214427,0.00008840653,0.000124348,0.03327619,0.1371699,0.7099264,0.000374047,0.009942728,0.002418211],"study_design_scores_gemma":[0.0005580977,0.0001882895,0.00372217,0.00004572239,0.00002213863,0.00001921434,0.003202368,0.06972151,0.8832253,0.001631052,0.03704504,0.0006191542],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9910591,0.000030171,0.00459937,0.000400774,0.001011265,0.0005179505,0.00003837527,0.00125614,0.00108684],"genre_scores_gemma":[0.9913663,0.000001745844,0.006070592,0.00001669923,0.00006271497,0.0003552569,0.0000807027,0.00003651133,0.002009445],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2945842,"threshold_uncertainty_score":0.6893486,"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."}}