{"id":"W3010954905","doi":"10.3390/buildings10030060","title":"Modeling of an Aerogel-Based “Thermal Break” for Super-Insulated Window Frames","year":2020,"lang":"en","type":"article","venue":"Buildings","topic":"Aerogels and thermal insulation","field":"Chemistry","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Regione Puglia","keywords":"Aerogel; Daylight; Window (computing); Architectural engineering; Thermal; Energy performance; Solar gain; Frame (networking); Efficient energy use; Building energy simulation; Computer science; Work (physics); Thermal comfort; Mechanical engineering; Materials science; Environmental science; Engineering physics; Engineering; Electrical engineering; Optics; Meteorology; Nanotechnology; Physics","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":[0.0000760839,0.0001671382,0.0002348879,0.00002195197,0.00008950716,0.00002903194,0.0002070169,0.0001713919,0.0002280779],"category_scores_gemma":[0.00005910068,0.0001677619,0.000127748,0.0001011456,0.0000339117,0.0001701599,0.00002843815,0.0001194975,0.00000430637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002053787,"about_ca_system_score_gemma":0.00004210576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001015629,"about_ca_topic_score_gemma":0.000001699732,"domain_scores_codex":[0.9989966,0.000009136921,0.0003082847,0.0002897426,0.0001666164,0.0002295801],"domain_scores_gemma":[0.9994251,0.00003592183,0.00009121937,0.0001888176,0.0001246759,0.0001342983],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002647961,0.00007350669,0.001131866,0.0001604192,0.00003508801,0.000002047364,0.0004325203,0.09931225,0.8954104,0.0004670253,0.000007437933,0.002702612],"study_design_scores_gemma":[0.0008144106,0.00004987928,0.00005866831,0.00003323885,0.00002700276,8.535938e-7,0.00006552636,0.6090213,0.3894301,0.0001194653,0.0002391138,0.0001404252],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9893332,0.00008663049,0.009637382,0.0002376769,0.00002472868,0.0001165922,0.00005847734,0.0001534362,0.0003519361],"genre_scores_gemma":[0.9945505,0.000001233033,0.00479641,0.0003028537,0.0001781663,0.0000157238,0.00008827911,0.00005121718,0.0000156269],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5097091,"threshold_uncertainty_score":0.6841131,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02841280752524766,"score_gpt":0.2513375610243441,"score_spread":0.2229247534990964,"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."}}