{"id":"W4313341345","doi":"10.1007/978-981-10-1775-9_4","title":"Performance-Driven Design Workflows","year":2022,"lang":"en","type":"book-chapter","venue":"SpringerBriefs in architectural design and technology","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Workflow; Dashboard; Computer science; Window (computing); Simple (philosophy); Range (aeronautics); Architectural design; Human–computer interaction; Architectural engineering; Systems engineering; Software engineering; Architecture; Engineering; Geography; Database; Aerospace engineering; Operating system","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001438767,0.0003954525,0.000397925,0.0009116949,0.0001197451,0.000020806,0.0003317615,0.0004688212,0.0002438709],"category_scores_gemma":[0.000009666445,0.0004205745,0.00004796259,0.0001937759,0.0001539288,0.00005770948,0.0001900287,0.001113735,0.000005669483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001323081,"about_ca_system_score_gemma":0.00003021287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003182744,"about_ca_topic_score_gemma":0.000005976755,"domain_scores_codex":[0.9987611,0.00002183369,0.0003144214,0.0003849866,0.0001463567,0.0003713662],"domain_scores_gemma":[0.9994547,0.00007638465,0.00006050791,0.0003443045,0.00001474278,0.00004931554],"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":[0.00002604373,0.000003723232,0.00003439879,0.00004823901,0.00004717866,0.0000367476,0.00003959374,0.8486511,0.0001500276,0.02644222,0.00007264373,0.1244481],"study_design_scores_gemma":[0.0007415522,0.00041289,0.000050702,0.0004327167,0.00006487832,0.0003282554,0.000004555834,0.8862566,0.001397668,0.0459463,0.06292678,0.001437137],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01554998,0.01099839,0.7529211,0.0008195412,0.002420039,0.003215549,0.00002022188,0.007535582,0.2065196],"genre_scores_gemma":[0.6163116,0.01062551,0.316327,0.0001571823,0.0002788204,0.0006700351,0.0000716299,0.0006887697,0.05486951],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6007616,"threshold_uncertainty_score":0.9998246,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01234421936969593,"score_gpt":0.1789186025272328,"score_spread":0.1665743831575369,"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."}}