{"id":"W2897178427","doi":"10.1016/j.engstruct.2018.10.026","title":"Irregularity index for quick identification of worst column removal scenarios of RC frame structures","year":2018,"lang":"en","type":"article","venue":"Engineering Structures","topic":"Structural Response to Dynamic Loads","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Frame (networking); Column (typography); Identification (biology); Index (typography); Structural engineering; Computer science; Mathematics; Engineering; Telecommunications","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":[0.0001909722,0.0003319521,0.0004655285,0.0003167291,0.00006175096,0.00002871052,0.0004495557,0.0002844355,0.00007703248],"category_scores_gemma":[0.0004793093,0.000338642,0.0001567175,0.00038165,0.0001819839,0.000145323,0.00005476193,0.0002455519,0.000001573211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001191894,"about_ca_system_score_gemma":0.00004590292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005156265,"about_ca_topic_score_gemma":0.00001199466,"domain_scores_codex":[0.9982128,0.00002100155,0.000699893,0.000301765,0.0003658072,0.0003987502],"domain_scores_gemma":[0.9987539,0.000181361,0.0001716905,0.0005797715,0.0002190117,0.00009425166],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002109746,0.000008876519,0.0004699667,0.001078225,0.0003020944,0.000003262318,0.00102764,0.4714618,0.5078677,0.01028727,0.0006781454,0.006604081],"study_design_scores_gemma":[0.001013744,0.0001359218,0.2293065,0.00009824175,0.00008756883,0.00006306773,0.00006961042,0.4575861,0.2985552,0.01037681,0.00204759,0.0006597072],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9099345,0.0003610245,0.08733876,0.00001687358,0.001447573,0.0004393663,0.0001358448,0.0002781974,0.00004789748],"genre_scores_gemma":[0.978436,0.000008957341,0.02104586,0.000009004755,0.0003175056,0.00001783513,0.00002808464,0.00008201927,0.00005471694],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2288365,"threshold_uncertainty_score":0.9999065,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005917085555377611,"score_gpt":0.2288977049666999,"score_spread":0.2229806194113223,"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."}}