{"id":"W2046330295","doi":"10.1007/s00894-008-0316-x","title":"Modeling of hydrogen-assisted cracking in iron crystal using a quasi-Newton method","year":2008,"lang":"en","type":"article","venue":"Journal of Molecular Modeling","topic":"Hydrogen embrittlement and corrosion behaviors in metals","field":"Materials Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Hydrogen embrittlement; Morse potential; Cracking; Materials science; Hydrogen; Context (archaeology); Crystal structure; Interatomic potential; Ultimate tensile strength; Molecular dynamics; Metallurgy; Crystallography; Chemistry; Physics; Computational chemistry; Composite material; Corrosion; Atomic physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003937929,0.0004922618,0.0008290752,0.0003641909,0.001033805,0.0006670652,0.002144161,0.002132209,0.005149214],"category_scores_gemma":[0.000845024,0.0006446906,0.0005470161,0.0004493733,0.0007870661,0.0007084562,0.0004283371,0.0007533266,0.0004123171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001126217,"about_ca_system_score_gemma":0.001976708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02341093,"about_ca_topic_score_gemma":0.01432867,"domain_scores_codex":[0.9998554,0.00004492008,0.000005051423,0.00001460796,0.00005073881,0.00002927938],"domain_scores_gemma":[0.9996092,0.0001809916,0.00003900231,0.00004442622,0.00008434121,0.00004207329],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003364974,0.00002754343,0.0002737864,0.00003965396,0.00001114016,0.0000914045,0.00003055763,0.9898919,0.001417017,0.006650311,0.0003957985,0.001137241],"study_design_scores_gemma":[0.000006903285,0.000004008023,0.00004811263,0.000001456726,0.000001025276,0.00000407328,0.000004143707,0.9994484,0.0001063719,0.000248852,0.0001245412,0.000002095539],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.570442,0.0007271622,0.3655142,0.001658813,0.000288605,0.0003170146,0.001277211,0.00094826,0.05882677],"genre_scores_gemma":[0.9266195,0.0003425116,0.06229512,0.000213859,0.00005297815,0.0003806302,0.0003678629,0.0002720316,0.009455407],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02341093,"threshold_uncertainty_score":0.04654932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06064974838972699,"score_gpt":0.3259566164374476,"score_spread":0.2653068680477206,"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."}}