{"id":"W2605562265","doi":"10.2166/ws.2017.044","title":"Integrated sensing technologies for detection and location of leaks in water distribution networks","year":2017,"lang":"en","type":"article","venue":"Water Science & Technology Water Supply","topic":"Water Systems and Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Leak; Leak detection; Emissivity; Environmental science; Thermography; Computer science; Relative humidity; Ground-penetrating radar; Flexibility (engineering); Remote sensing; Infrared; Radar; Environmental engineering; Geology; Statistics; Meteorology; Telecommunications; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003962562,0.0003545477,0.0003762667,0.0006120026,0.0001282842,0.0004019252,0.0004179723,0.0003589441,0.000466808],"category_scores_gemma":[0.0006200133,0.0002119274,0.0002167938,0.0006242843,0.0001810466,0.0006776978,0.0003950154,0.0002720934,0.0001195977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002627431,"about_ca_system_score_gemma":0.0001532295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005651792,"about_ca_topic_score_gemma":0.0008022854,"domain_scores_codex":[0.9995199,0.000110114,0.00002094408,0.00009598949,0.0002211639,0.00003181383],"domain_scores_gemma":[0.9997173,0.00007440455,0.00008268887,0.0000242871,0.00009348741,0.00000782673],"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":[0.0003681734,0.0002180574,0.01204899,0.0003822697,0.00008342187,0.0001379724,0.0002277711,0.04824067,0.6996626,0.001019087,0.0005381001,0.2370729],"study_design_scores_gemma":[0.00002381752,0.00109349,0.01661266,0.00005285013,0.000112696,0.0002231059,0.0002910656,0.6256962,0.3523253,0.001160708,0.002353853,0.0000541996],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5046952,0.001540407,0.4904675,0.0001169882,0.00006344231,0.0000675876,0.0001275,0.0008305837,0.002090781],"genre_scores_gemma":[0.9468744,0.0003532378,0.05215929,0.00002401184,0.000008655357,0.00002857406,0.00003669636,0.00000926875,0.0005059041],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006120026,"threshold_uncertainty_score":0.00209558,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006174914645484893,"score_gpt":0.1973533686076173,"score_spread":0.1911784539621324,"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."}}