{"id":"W4402135375","doi":"10.1007/978-3-031-61539-9_28","title":"Geometrical and Material Characterization of Old Industrial Masonry Buildings in Eastern Canada","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Masonry and Concrete Structural Analysis","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Masonry; Characterization (materials science); Geology; Forensic engineering; Engineering; Materials science; Structural engineering; Nanotechnology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0001620812,0.0003316607,0.0003041017,0.005006544,0.002786309,0.00157868,0.001161023,0.0002365484,0.003285106],"category_scores_gemma":[0.0005754461,0.0002913717,0.0002777374,0.007674589,0.001168741,0.0002403821,0.0006115271,0.0003181378,0.0005012593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01569647,"about_ca_system_score_gemma":0.008166187,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9868903,"about_ca_topic_score_gemma":0.9965918,"domain_scores_codex":[0.9996233,0.0000139589,0.00001739259,0.00005467858,0.0001626516,0.0001279923],"domain_scores_gemma":[0.9994099,0.00005143343,0.00005466279,0.00002171838,0.0003710606,0.0000911712],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004907474,0.0001519318,0.8219049,0.0002573095,0.00008548528,0.001670296,0.008965516,0.0151163,0.01215844,0.004896065,0.004025241,0.1302778],"study_design_scores_gemma":[0.000001433163,0.000008709123,0.9909289,0.0000175619,0.000009218384,0.000126322,0.002548661,0.001456733,0.0004371607,0.0000722242,0.004381054,0.00001200976],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9784049,0.0004692911,0.0003732828,0.00003130624,0.000004049883,0.0000267148,0.00275526,0.00002944868,0.01790571],"genre_scores_gemma":[0.9882272,0.0002663466,0.0005806022,0.000008754199,0.000002463226,0.00000752651,0.001641292,0.00001904508,0.0092467],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01569647,"threshold_uncertainty_score":0.1138864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007126037550340693,"score_gpt":0.1686945312541421,"score_spread":0.1615684937038014,"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."}}