{"id":"W1967379861","doi":"10.1016/j.enbuild.2010.04.006","title":"A decision tree method for building energy demand modeling","year":2010,"lang":"en","type":"article","venue":"Energy and Buildings","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":614,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Categorical variable; Decision tree; Computer science; Data mining; Tree (set theory); Energy consumption; Energy (signal processing); Flowchart; Rank (graph theory); Artificial intelligence; Machine learning; Engineering; Statistics; 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":[0.00140507,0.0006229607,0.001547417,0.0007241154,0.0005099465,0.0007115002,0.001303671,0.0009663997,0.004317713],"category_scores_gemma":[0.002561618,0.0006106505,0.001228822,0.001480469,0.0002720047,0.001016412,0.0005888725,0.001232788,0.0007841865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004959693,"about_ca_system_score_gemma":0.001143537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009551926,"about_ca_topic_score_gemma":0.008480787,"domain_scores_codex":[0.9992232,0.000392049,0.00004339224,0.00009029957,0.0001991751,0.00005186705],"domain_scores_gemma":[0.9984589,0.001240155,0.00004057844,0.00004674855,0.0001675484,0.00004608672],"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":[0.00004602117,0.00008296422,0.000272334,0.00006997357,0.00006024418,0.00003703687,0.00002663679,0.8935294,0.0005985982,0.01044821,0.001703286,0.0931254],"study_design_scores_gemma":[0.000005044399,0.000007131336,0.00002301694,0.000002974626,0.00000598903,0.000004607086,0.000001657172,0.9964162,0.00006542045,0.003124268,0.0003413693,0.000002342854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002331062,0.0001124709,0.9966582,0.00004477756,0.00002300901,0.00002042169,0.00009621886,0.0001547138,0.0005590614],"genre_scores_gemma":[0.180047,0.0005172618,0.8151454,0.0001129623,0.0001016527,0.0003001466,0.0006358064,0.0001402646,0.0029995],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009551926,"threshold_uncertainty_score":0.0189926,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00621407784484555,"score_gpt":0.2270800704649438,"score_spread":0.2208659926200982,"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."}}