{"id":"W2777726521","doi":"10.4995/vitruvio-ijats.2017.7687","title":"Selecting and Installing Energy-Efficient Windows to Improve Dwelling Sustainability","year":2017,"lang":"en","type":"article","venue":"VITRUVIO - International Journal of Architectural Technology and Sustainability","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Overheating (electricity); Building envelope; Architectural engineering; Installation; Sustainability; Solar gain; Energy consumption; Facade; Computer science; Engineering; Environmental science; Civil engineering; Solar energy; Operating system; Meteorology; Electrical engineering","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":[0.0004625442,0.0001773296,0.0002412875,0.0005364285,0.0003571434,0.0001440396,0.0004645157,0.0001541852,0.000003494583],"category_scores_gemma":[0.0009526251,0.0001592579,0.00006342153,0.0001401424,0.0002437054,0.0001915575,0.000253258,0.0004300528,5.890948e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003182989,"about_ca_system_score_gemma":0.00008863261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004154553,"about_ca_topic_score_gemma":0.00002118814,"domain_scores_codex":[0.9988772,0.00002826076,0.0004005957,0.0002279434,0.0001934239,0.0002725933],"domain_scores_gemma":[0.9986554,0.00007779249,0.0001865128,0.0002398347,0.0007388197,0.0001016901],"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":[0.0001721619,0.00003877504,0.0141207,0.00007082585,0.0001136032,0.00004255579,0.0004467602,0.6939558,0.001140675,0.01275167,0.000003140088,0.2771434],"study_design_scores_gemma":[0.002271543,0.0006930724,0.01970945,0.0001991339,0.00008650715,0.001015258,0.00265729,0.6597077,0.0412144,0.2686853,0.002779594,0.0009807474],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9065108,0.0001688504,0.09003464,0.002631286,0.0003808755,0.0001148173,0.000001828877,0.00008503245,0.00007189678],"genre_scores_gemma":[0.9971833,0.0000346421,0.002589629,0.00003505565,0.0001087855,0.000009227902,8.992088e-7,0.00001488759,0.00002352479],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2761626,"threshold_uncertainty_score":0.6494349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003530811807692529,"score_gpt":0.2342076245460407,"score_spread":0.2306768127383481,"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."}}