{"id":"W2051210587","doi":"10.1002/nme.3112","title":"An adaptive concurrent multiscale method for the dynamic simulation of dislocations","year":2011,"lang":"en","type":"article","venue":"International Journal for Numerical Methods in Engineering","topic":"Microstructure and mechanical properties","field":"Materials Science","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Army Research Office; Office of Naval Research","keywords":"Classification of discontinuities; Void (composites); Statistical physics; Dislocation; Finite element method; Discontinuity (linguistics); Enhanced Data Rates for GSM Evolution; Multiscale modeling; Materials science; Computer science; Physics; Mathematics; Mathematical analysis; Thermodynamics; Computational chemistry; Chemistry","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":[0.001027633,0.00009370464,0.0001607328,0.00007757227,0.00005469781,0.0000273995,0.00038857,0.00004457248,0.00005472907],"category_scores_gemma":[0.0007470704,0.00006285022,0.00009997733,0.00006233334,0.00002406795,0.0001741325,0.00003091534,0.0001252813,5.378974e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007084639,"about_ca_system_score_gemma":0.00002130363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002355329,"about_ca_topic_score_gemma":0.000002696911,"domain_scores_codex":[0.9991051,0.00009403883,0.0003874594,0.0001343435,0.0001342152,0.0001448512],"domain_scores_gemma":[0.998332,0.001126319,0.0001435803,0.00009310424,0.0002549732,0.00005003677],"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":[0.0001577728,0.00005511883,0.000005722749,0.00001119764,0.00003133025,3.951049e-7,0.0006671362,0.3956849,0.4931884,0.003172574,0.000003553459,0.1070219],"study_design_scores_gemma":[0.000293673,0.0001470585,0.000146212,0.00002891416,0.00001906543,0.000009895427,0.0001083352,0.8724462,0.1229738,0.003162501,0.0005892909,0.00007507749],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002326112,0.0001651943,0.9944975,0.00005534273,0.002647872,0.0002509307,0.00003624237,0.00001479475,0.000006014057],"genre_scores_gemma":[0.463995,0.000005242828,0.5358608,0.00001702142,0.0000727837,0.00003186627,0.000001608653,0.000009242575,0.000006508759],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4767613,"threshold_uncertainty_score":0.2562957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06824895565017348,"score_gpt":0.4316202876728171,"score_spread":0.3633713320226436,"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."}}