{"id":"W4200008981","doi":"10.1088/1742-6596/2069/1/012230","title":"Selecting durable building envelope systems with machine learning assisted hygrothermal simulations database","year":2021,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"UT-Battelle; Battelle; U.S. Department of Energy","keywords":"Durability; Envelope (radar); Building envelope; Computer science; Parametric statistics; Artificial neural network; Machine learning; Expert system; Artificial intelligence; Database","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":[0.00009828265,0.0001251678,0.0002075699,0.00004596891,0.0001595788,0.0001519137,0.00008741429,0.00003688858,0.00002815312],"category_scores_gemma":[0.00003213499,0.0001121464,0.00003337641,0.0002967219,0.00001967328,0.0007725038,0.00001927539,0.0003215143,3.479162e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003928138,"about_ca_system_score_gemma":0.0001199356,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001001149,"about_ca_topic_score_gemma":0.00001788649,"domain_scores_codex":[0.9992861,0.00004582305,0.0002541997,0.00008408173,0.0001782477,0.0001515482],"domain_scores_gemma":[0.9992916,0.00005234872,0.0001586363,0.00009579217,0.000352419,0.00004922513],"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":[0.00001577962,0.00001419489,0.0009520268,0.00005410456,0.00009314447,0.0000215192,0.0001444387,0.9323875,0.05979347,0.004024519,0.000007386848,0.002491915],"study_design_scores_gemma":[0.0004853288,0.00008784655,0.0002832031,0.000505285,0.00007491452,0.0003420946,0.0003663884,0.7474045,0.2489519,0.0001830827,0.001023208,0.0002921834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3034804,0.0003093853,0.6952414,0.00004138581,0.0002505592,0.00003969964,0.000009493475,0.00008908979,0.0005386071],"genre_scores_gemma":[0.9849279,0.00008900739,0.01458986,0.000006276447,0.00014087,0.000001576887,0.00002450527,0.00002742381,0.000192559],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6814475,"threshold_uncertainty_score":0.4573195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01746243258968141,"score_gpt":0.2163557487484334,"score_spread":0.1988933161587519,"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."}}