{"id":"W2075451481","doi":"10.1016/j.enbuild.2006.11.008","title":"Applying uncertainty considerations to energy conservation equations","year":2007,"lang":"en","type":"article","venue":"Energy and Buildings","topic":"Numerical Methods and Algorithms","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Monte Carlo method; Computer science; Uncertainty quantification; Conservation of energy; Energy conservation; Mathematical optimization; Uncertainty analysis; Sensitivity analysis; Energy (signal processing); Industrial engineering; Simulation; Engineering; Mathematics; Machine learning; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004117211,0.00009718252,0.0001114821,0.0001132314,0.0003165185,0.0001374728,0.0001316577,0.00004773897,0.00001458642],"category_scores_gemma":[0.0001916243,0.00008948791,0.00002780671,0.0003960548,0.0000353875,0.0002336322,0.0001068266,0.0000452137,0.000001589768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002253842,"about_ca_system_score_gemma":0.0000305808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007477202,"about_ca_topic_score_gemma":0.0001248012,"domain_scores_codex":[0.9991218,0.00003936946,0.0001962365,0.0002808156,0.0001401681,0.0002215648],"domain_scores_gemma":[0.998913,0.0006261632,0.00005228085,0.0001721802,0.00008352256,0.0001528178],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000001419498,0.00001159537,0.00002476378,8.016277e-7,0.000004544005,0.000003601352,0.00007089713,0.0002769422,0.00386561,0.7615144,0.0004683691,0.233757],"study_design_scores_gemma":[0.0003196542,0.0001213633,0.0003801275,0.00002834686,0.000009250521,0.00002964165,0.00007300971,0.1013955,0.02109385,0.3066027,0.5695293,0.0004173357],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001377338,0.00007600273,0.9937226,0.003338358,0.0001982762,0.00004587188,9.469705e-7,0.00009312442,0.001147503],"genre_scores_gemma":[0.3748707,0.0000131914,0.6155331,0.009107933,0.0001149507,0.00004830508,0.000002310334,0.000005863646,0.0003037111],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5690609,"threshold_uncertainty_score":0.3649211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02431116221979534,"score_gpt":0.2816772244617821,"score_spread":0.2573660622419867,"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."}}