{"id":"W2281963269","doi":"10.5353/th_b5677219","title":"The statistic energy profile analysis and carbon renovation plan of the household energy use : taking the Region of Waterloo as an example","year":2015,"lang":"en","type":"dissertation","venue":"","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Statistic; Plan (archaeology); Energy (signal processing); Carbon fibers; Geography; Statistics; Environmental science; Computer science; Mathematics; Archaeology; Algorithm","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0003011835,0.0002952283,0.0002073059,0.0010563,0.000393272,0.0006717025,0.0002588123,0.0001626855,0.001566057],"category_scores_gemma":[0.0006986184,0.0001093106,0.0002724233,0.001495669,0.0001610039,0.0003752139,0.0002122734,0.0001535097,0.0002517253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002217723,"about_ca_system_score_gemma":0.001982279,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2472629,"about_ca_topic_score_gemma":0.3349296,"domain_scores_codex":[0.9998012,0.00002873216,0.000009739149,0.0000284207,0.0001017941,0.00003005012],"domain_scores_gemma":[0.9997759,0.00002695401,0.00002315334,0.0000147249,0.0001470509,0.0000122159],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000442362,0.0002348677,0.2578147,0.0002054426,0.00007544725,0.0005163102,0.001235868,0.2942346,0.01794053,0.01558822,0.015035,0.3966766],"study_design_scores_gemma":[0.00001795205,0.0003451125,0.4276704,0.00004122653,0.00005055007,0.0001420104,0.004444616,0.5248448,0.01617863,0.003422057,0.02275842,0.0000841307],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8531341,0.0002584919,0.09493389,0.0004866788,0.00002206774,0.0004678963,0.008428285,0.0007105281,0.04155812],"genre_scores_gemma":[0.9582114,0.0001936092,0.02943145,0.00001690237,0.00000364206,0.00009074827,0.003590069,0.00004083874,0.008421302],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7527371,"threshold_uncertainty_score":0.4916473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03061727161271753,"score_gpt":0.2148234834274045,"score_spread":0.1842062118146869,"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."}}