{"id":"W2912119416","doi":"10.3384/ecp19157543","title":"Modeling of Low Temperature Thermal Networks Using Historical Building Data from District Energy Systems","year":2019,"lang":"en","type":"article","venue":"Linköping electronic conference proceedings","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Centres of Excellence","keywords":"Microgrid; Environmental science; Thermal; Period (music); Energy (signal processing); Water heating; Computer science; Civil engineering; Architectural engineering; Meteorology; Engineering; Electrical engineering; Renewable energy; Waste management; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001714044,0.0002748732,0.0003955846,0.0001100481,0.00008032651,0.0001261998,0.0006650158,0.0002924407,0.00001981692],"category_scores_gemma":[0.00001556468,0.0002835372,0.00005419507,0.0003540102,0.00001341547,0.0004897033,0.0001472737,0.0004995904,5.208985e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004456345,"about_ca_system_score_gemma":0.0001229463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000389436,"about_ca_topic_score_gemma":0.000009198547,"domain_scores_codex":[0.9983288,0.00001012686,0.0004220054,0.0004550529,0.0002402499,0.0005437619],"domain_scores_gemma":[0.9993094,0.00003083573,0.000107719,0.0003396511,0.00014327,0.00006909051],"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":[0.00001598861,0.0000131177,0.0003233637,0.00008489385,0.0000696034,3.659623e-7,0.00005684868,0.963325,0.03148693,0.004170368,0.00004070557,0.000412788],"study_design_scores_gemma":[0.0002192143,0.00002752347,0.000008023077,0.0003718904,0.0000402769,0.000004554737,0.00003725113,0.9974881,0.00118593,0.0001349649,0.0001692779,0.0003129605],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7463335,0.002685101,0.2498428,0.0000114071,0.0005249926,0.000103876,0.00000692101,0.0002657474,0.000225626],"genre_scores_gemma":[0.9982319,0.0003230255,0.0009023752,0.00001233042,0.0003093004,0.000009506518,0.0000952011,0.0000612677,0.00005504392],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2518984,"threshold_uncertainty_score":0.9999617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0148429545038409,"score_gpt":0.1992481085818585,"score_spread":0.1844051540780176,"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."}}