{"id":"W4412699923","doi":"10.11159/ffhmt25.220","title":"Integration of Hydraulic and Thermal Sensors with Machine Learning For Enhanced Leak Detection and Localization in District Heating Systems","year":2025,"lang":"en","type":"article","venue":"Proceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer","topic":"Seismology and Earthquake Studies","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Leak; Leak detection; Thermal hydraulics; Thermal; Computer science; Hydraulic machinery; Environmental science; Embedded system; Automotive engineering; Engineering; Mechanical engineering; Environmental engineering; Heat transfer; Physics; Mechanics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001912692,0.00009327012,0.0001472182,0.0001074881,0.00008565299,0.00004982483,0.0001062825,0.00004934224,7.120403e-7],"category_scores_gemma":[0.00005401627,0.00006482477,0.00001778185,0.0001244079,0.00006248937,0.0001724502,0.00002579933,0.0000988921,3.947591e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001840341,"about_ca_system_score_gemma":0.00001274825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001090323,"about_ca_topic_score_gemma":0.00005058541,"domain_scores_codex":[0.9994205,0.00001243238,0.0001805131,0.0001862745,0.0001154212,0.00008490021],"domain_scores_gemma":[0.9996465,0.00007548539,0.00002935107,0.00003013117,0.0002051146,0.00001335601],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007552474,0.00005562327,0.02691685,0.0004013389,0.0001438373,2.392843e-7,0.003495265,0.006416299,0.8139501,0.124997,0.000005339602,0.02286285],"study_design_scores_gemma":[0.0006997609,0.0002051946,0.006807704,0.0003472221,0.00001698821,0.000002806865,0.0004162471,0.8539731,0.1367064,0.0007169486,0.00003131281,0.00007632853],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8269158,0.0001060852,0.1710295,0.0007074271,0.00009704562,0.0002124748,0.000002317984,0.00001214769,0.0009171924],"genre_scores_gemma":[0.9994404,0.0001330826,0.00026333,0.0000444246,0.000009777544,0.00002639238,0.000001159007,0.000002987682,0.00007844021],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8475568,"threshold_uncertainty_score":0.2643477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01341951754399415,"score_gpt":0.2250237720969285,"score_spread":0.2116042545529344,"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."}}