{"id":"W3088569216","doi":"10.23967/dbmc.2020.095","title":"Metamodel Development for Predicting Hygrothermal Performance of Wood-Frame Wall under Rain Leakage","year":2020,"lang":"en","type":"article","venue":"XV International Conference on Durability of Building Materials and Components. eBook of Proceedings","topic":"Hygrothermal properties of building materials","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Metamodeling; Leakage (economics); Stochastic modelling; Frame (networking); Environmental science; Computer science; Moisture; Artificial neural network; Mathematics; Meteorology; Statistics; Artificial intelligence; Geography","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":[0.0005913455,0.0002648452,0.0006062968,0.00009990171,0.00005229747,0.00005879139,0.0004316454,0.0001006899,0.0001375957],"category_scores_gemma":[0.00007168472,0.000251684,0.00007042623,0.0000390727,0.0001632591,0.0002630117,0.000128513,0.00008819447,0.000001317894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005277646,"about_ca_system_score_gemma":0.00003411725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002752811,"about_ca_topic_score_gemma":3.47497e-7,"domain_scores_codex":[0.9981075,0.00001743128,0.0009665157,0.0003166111,0.0003586172,0.0002333071],"domain_scores_gemma":[0.9990789,0.00005243476,0.0003610576,0.00009724566,0.0003283922,0.00008197477],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005884335,0.0000472553,0.0005697838,0.00197601,0.0001450493,8.650696e-8,0.0009881863,0.001330538,0.9894043,0.004439134,0.00001042379,0.0005008397],"study_design_scores_gemma":[0.0007057308,0.0002178181,0.002016375,0.000643674,0.00002302137,0.000001634533,0.00009097641,0.04632386,0.9491656,0.0004869992,0.0001015513,0.0002227465],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973733,0.00001785183,0.0009064496,0.0003397096,0.0001819511,0.0005535265,0.00007356399,0.00008469595,0.0004689922],"genre_scores_gemma":[0.993813,0.00002945405,0.005908764,0.00006854477,0.0000589515,0.00005949044,0.00001006085,0.00004043869,0.00001127827],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04499332,"threshold_uncertainty_score":0.9999936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05331615292499449,"score_gpt":0.2417633172928568,"score_spread":0.1884471643678623,"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."}}