{"id":"W2021954849","doi":"10.4028/www.scientific.net/amr.834-836.683","title":"Learn from Tradition: Utilizing Traditional Building Materials in the Post-Earthquake Reconstruction","year":2013,"lang":"en","type":"article","venue":"Advanced materials research","topic":"Disaster Management and Resilience","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Architectural engineering; Quake (natural phenomenon); Engineering; China; Construction engineering; Order (exchange); Process (computing); Civil engineering; Computer science; Business; History; Geology","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002474688,0.0001174835,0.0001798118,0.0001949909,0.0007815048,0.0008143985,0.000556434,0.00008835997,0.00678799],"category_scores_gemma":[0.0003403206,0.00009286889,0.00003057089,0.0004289,0.0005467496,0.001294097,0.00005976902,0.0001764706,0.000388114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008927391,"about_ca_system_score_gemma":0.00008349981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004360912,"about_ca_topic_score_gemma":0.0004396536,"domain_scores_codex":[0.996455,0.001403347,0.000342493,0.0003560421,0.0008752195,0.000567873],"domain_scores_gemma":[0.9989398,0.0005171526,0.00007609666,0.0002311175,0.000161446,0.00007434667],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000665557,0.00008511364,0.0001402233,0.00002770542,0.00001083137,0.00001315217,0.006267312,0.0000137871,0.8620646,0.08240066,0.0006482116,0.04826186],"study_design_scores_gemma":[0.00189899,0.0003163496,0.1863518,0.0006088195,0.00001932174,0.00001672292,0.1880405,0.00002071174,0.1996177,0.3970588,0.02516575,0.0008845633],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9771868,0.00004359775,0.00001553186,0.003413965,0.0008502309,0.0008863082,0.00004883233,0.00004657053,0.01750816],"genre_scores_gemma":[0.9975904,0.0001599874,0.0007718939,0.0001347908,0.0006194129,0.0002853909,0.00005409245,0.00001285627,0.0003711888],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6624469,"threshold_uncertainty_score":0.9941199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1032021856316716,"score_gpt":0.3673527105051782,"score_spread":0.2641505248735066,"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."}}