{"id":"W7081203655","doi":"10.1016/j.ijhydene.2025.151470","title":"Towards the understanding of hydrogen embrittlement in pipeline steels using EBSD","year":2025,"lang":"en","type":"article","venue":"International Journal of Hydrogen Energy","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"BC Innovation Council; National Research Council Canada","funders":"Office of Energy Research and Development; National Research Council Canada; Higher Education Discipline Innovation Project","keywords":"Electron backscatter diffraction; Hydrogen; Hydrogen embrittlement; Ultimate tensile strength; Embrittlement; Helium; Texture (cosmology); Tensile testing","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005029584,0.0003976173,0.000712202,0.001125702,0.0004730208,0.001683711,0.001719394,0.001364764,0.003242012],"category_scores_gemma":[0.001312181,0.0004933042,0.0005190985,0.001038877,0.000982015,0.002850429,0.001142814,0.0009500387,0.0006693435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001027599,"about_ca_system_score_gemma":0.000944095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01621083,"about_ca_topic_score_gemma":0.01144574,"domain_scores_codex":[0.9997969,0.00002775467,0.00001656154,0.00005743847,0.00007127305,0.00003009439],"domain_scores_gemma":[0.9995732,0.00009188181,0.00004996209,0.00009078209,0.0001797131,0.00001449415],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001170509,0.0002007485,0.01898636,0.0005531955,0.00008281524,0.0006205196,0.0007073575,0.7294356,0.07927796,0.08198015,0.001259131,0.08677913],"study_design_scores_gemma":[0.00000891626,0.00002804444,0.005117238,0.00004875451,0.00001640711,0.0001002354,0.000374315,0.95057,0.01094184,0.02529691,0.007469715,0.00002765957],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1721859,0.00271066,0.787297,0.001018386,0.0001289218,0.0001035831,0.0008623895,0.001005716,0.03468744],"genre_scores_gemma":[0.8709355,0.002211194,0.1194293,0.0001262102,0.00003377004,0.0000507705,0.0005022156,0.0001249825,0.006586047],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01621083,"threshold_uncertainty_score":0.03223294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04064952145003394,"score_gpt":0.2816276701791635,"score_spread":0.2409781487291296,"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."}}