{"id":"W4410840058","doi":"10.1016/j.engappai.2025.111127","title":"Fire resistance rating prediction of timber-to-steel connections and design optimization informed by explainable machine learning","year":2025,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Wood Treatment and Properties","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"University of British Columbia; China Scholarship Council","keywords":"Computer science; Fire resistance; Resistance (ecology); Machine learning; Artificial intelligence; Rating system; Composite material","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005816012,0.0005869223,0.0005773263,0.000557228,0.0001947886,0.0005076596,0.0006352675,0.0009022081,0.001397079],"category_scores_gemma":[0.00264141,0.0005093917,0.0006626004,0.0002873347,0.0004141625,0.0006275029,0.000247942,0.0007643062,0.00017259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006562137,"about_ca_system_score_gemma":0.0005039988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008834152,"about_ca_topic_score_gemma":0.01119854,"domain_scores_codex":[0.9998541,0.00005220513,0.000006713978,0.0000435176,0.00002367336,0.00001973117],"domain_scores_gemma":[0.9985985,0.001103232,0.0001179142,0.00005403045,0.0001010475,0.00002513543],"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":[0.00002033051,0.00001744906,0.0007360201,0.000009242485,0.000009129782,0.00001714981,0.000007359951,0.9936932,0.0004142285,0.0004196173,0.0001054417,0.00455089],"study_design_scores_gemma":[8.49737e-7,0.000002148975,0.0001233189,4.158884e-7,9.856971e-7,7.732281e-7,5.258033e-7,0.9996256,0.0000662363,0.000169483,0.000009169749,5.900535e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5656266,0.000366659,0.4297496,0.0003834109,0.00004929485,0.00003345963,0.0002338602,0.0006482371,0.002908808],"genre_scores_gemma":[0.9851213,0.00003421849,0.01392032,0.00002062269,0.000009272823,0.00001570964,0.0001179054,0.00002036195,0.0007403206],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008834152,"threshold_uncertainty_score":0.01756549,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01452854216527377,"score_gpt":0.2205948396494814,"score_spread":0.2060662974842076,"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."}}