{"id":"W1985267739","doi":"10.1016/j.msea.2009.01.072","title":"A Markov Chain–Monte Carlo model for intergranular stress corrosion crack propagation in polycrystalline materials","year":2009,"lang":"en","type":"article","venue":"Materials Science and Engineering A","topic":"Hydrogen embrittlement and corrosion behaviors in metals","field":"Materials Science","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Grain boundary; Materials science; Intergranular corrosion; Fracture mechanics; Crack closure; Crack growth resistance curve; Stress corrosion cracking; Monte Carlo method; Stress concentration; Mechanics; Structural engineering; Microstructure; Composite material; Corrosion; Mathematics; Physics; Statistics; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002180921,0.0002614638,0.0003884813,0.0002870623,0.0001832006,0.0004535993,0.0003934631,0.00008716673,0.00006537085],"category_scores_gemma":[0.0001590485,0.0002326929,0.00003042493,0.0002984559,0.0001161845,0.0005949803,0.0001025777,0.00005117748,0.000006770454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009564312,"about_ca_system_score_gemma":0.0000696662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003402805,"about_ca_topic_score_gemma":0.000007458354,"domain_scores_codex":[0.9977753,0.00003299878,0.0005665374,0.0005539732,0.0004703492,0.0006008493],"domain_scores_gemma":[0.9993094,0.000020891,0.0001252915,0.0002811081,0.00013625,0.0001270966],"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":[0.00007399568,0.00005133014,0.00002175184,0.00006645867,3.576654e-7,0.000003679963,0.000294069,0.003982428,0.9949691,0.0001776274,0.00002714569,0.0003320108],"study_design_scores_gemma":[0.0004652758,0.0001289562,0.0002859059,0.0001935074,0.00001298082,0.000007118849,0.0000412575,0.07015215,0.9283191,0.00008201169,0.00003246047,0.0002793085],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9886184,0.0000653874,0.009528564,0.0001291426,0.0007277345,0.0006716456,0.0001444413,0.0001083542,0.000006275462],"genre_scores_gemma":[0.9954194,0.00003431222,0.004101576,0.00009313982,0.00009172185,0.0001300642,0.00001551396,0.00002088953,0.00009339774],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06665008,"threshold_uncertainty_score":0.9488943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01095728376518096,"score_gpt":0.2392665352087879,"score_spread":0.2283092514436069,"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."}}