{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004771433,0.0004022729,0.0003831354,0.0003082723,0.0001876725,0.0004714071,0.0003546552,0.0003823029,0.0004325024],"category_scores_gemma":[0.001263662,0.000244839,0.0002463596,0.0002910203,0.0002371306,0.00073649,0.0003334224,0.0004297925,0.00009108771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005710783,"about_ca_system_score_gemma":0.0003948832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004834902,"about_ca_topic_score_gemma":0.00598819,"domain_scores_codex":[0.999801,0.00007873208,0.00001188513,0.00003944276,0.00004924757,0.00001975015],"domain_scores_gemma":[0.9995099,0.0002865751,0.00006202741,0.00003916612,0.00008704154,0.00001514911],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001109547,0.00006909369,0.003190721,0.0000449249,0.00001959423,0.00003955314,0.00003006982,0.9377175,0.007950683,0.0002381393,0.0001817322,0.05040702],"study_design_scores_gemma":[0.000001778388,0.0000212847,0.0006658728,0.000001743269,0.000002814275,0.000003560598,0.000005001281,0.9971544,0.001934838,0.0001332939,0.00007278615,0.000002648173],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4927593,0.0004675945,0.5020374,0.0003648077,0.00006074582,0.00005300745,0.000123177,0.001455252,0.002678817],"genre_scores_gemma":[0.9869804,0.00004570106,0.01264794,0.00001281678,0.000005308613,0.000009527404,0.00003132686,0.000008391515,0.000258658],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004834902,"threshold_uncertainty_score":0.009613514,"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."}}