{"id":"W4405979414","doi":"10.1080/15376494.2024.2443818","title":"Finite element analysis and multi-stage cooperative optimization of the expansion-tearing energy absorption structure","year":2024,"lang":"en","type":"article","venue":"Mechanics of Advanced Materials and Structures","topic":"Cellular and Composite Structures","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Education and Child Care","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Tearing; Finite element method; Stage (stratigraphy); Absorption (acoustics); Element (criminal law); Energy (signal processing); Structural engineering; Materials science; Mechanical engineering; Engineering; Physics; Composite material; 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":[],"consensus_categories":[],"category_scores_codex":[0.00004143805,0.0001514325,0.0002536751,0.0001087929,0.00005458382,0.00005375101,0.00007287986,0.00006733196,0.0000970622],"category_scores_gemma":[0.000008448914,0.0001053204,0.00004400074,0.000192141,0.00002247943,0.00008343413,0.00006097348,0.00005202997,1.949558e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001115386,"about_ca_system_score_gemma":0.000006947113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002747137,"about_ca_topic_score_gemma":0.00004880214,"domain_scores_codex":[0.9993439,0.00002755512,0.0002500038,0.0001677684,0.0001099311,0.0001008129],"domain_scores_gemma":[0.9997106,0.00002383863,0.00005569165,0.0001459612,0.00003932591,0.00002460661],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000009156578,8.873401e-7,0.000003076465,0.0001059422,0.00009730634,3.766858e-7,0.0001559636,0.4913799,0.5024768,0.004620843,6.806281e-7,0.001149152],"study_design_scores_gemma":[0.0001292118,0.00001854611,0.0002120567,0.00002940197,0.0001101235,0.00000151514,0.00006063072,0.314778,0.6827803,0.001753772,0.00003759328,0.00008889315],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6978452,0.00155185,0.2997618,0.000005386873,0.0003938637,0.0001083439,0.0002893045,0.00003719786,0.000007005775],"genre_scores_gemma":[0.9919492,0.0005672127,0.007368332,0.000006701572,0.0000218108,0.000003288572,0.00004864735,0.00001688538,0.00001792137],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.294104,"threshold_uncertainty_score":0.4294842,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006085609927227328,"score_gpt":0.2158724149307645,"score_spread":0.2097868050035372,"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."}}