{"id":"W1511737461","doi":"","title":"Comparing Masonry Compressive Strength in Various Codes","year":2005,"lang":"en","type":"article","venue":"ACI Concrete International","topic":"Masonry and Concrete Structural Analysis","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Masonry; Compressive strength; Mortar; Structural engineering; Geotechnical engineering; Building code; Materials science; Engineering; Composite material","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001576939,0.0001781717,0.000227588,0.003041364,0.0004708129,0.00051824,0.0004465019,0.0002269163,0.0011366],"category_scores_gemma":[0.008009175,0.0001823842,0.0002122548,0.002583988,0.0008484728,0.0004093836,0.0004750476,0.0002422954,0.0001800677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001367079,"about_ca_system_score_gemma":0.0009622515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02783969,"about_ca_topic_score_gemma":0.136525,"domain_scores_codex":[0.9969914,0.0003009504,0.0001560517,0.0001410669,0.002264701,0.0001457599],"domain_scores_gemma":[0.9938877,0.001305625,0.0007625689,0.000342715,0.003485988,0.0002155051],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007427047,0.0001933861,0.68041,0.0004847772,0.0002337197,0.00040746,0.003416334,0.01611434,0.1303006,0.006229247,0.0007642372,0.1607031],"study_design_scores_gemma":[0.00001174334,0.0006888375,0.9511739,0.00004517392,0.00003931547,0.0003471328,0.00159316,0.00384947,0.03781024,0.0005728087,0.003822563,0.00004570358],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9912962,0.0001106314,0.002181015,0.000008959264,0.000005301187,0.00002015612,0.0001573337,0.00002679524,0.006193586],"genre_scores_gemma":[0.9953406,0.0001267062,0.003027376,0.000008735191,0.000002134588,0.00001769944,0.0003592059,0.00001296519,0.001104592],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02783969,"threshold_uncertainty_score":0.05535531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01030562909960344,"score_gpt":0.2242753395029899,"score_spread":0.2139697104033865,"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."}}