{"id":"W2513715564","doi":"10.1149/ma2016-02/13/1301","title":"(Henry B. Linford Award for Distinguished Teaching) Determination of Local Hydrogen Concentrations in High Performance Alloys Using Local Probe Methods","year":2016,"lang":"en","type":"article","venue":"ECS Meeting Abstracts","topic":"Advanced Materials Characterization Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Kelvin probe force microscope; Hydrogen; Hydrogen embrittlement; Atom probe; Micrometer; Analytical Chemistry (journal); Scanning electrochemical microscopy; Materials science; Thermal diffusivity; Scanning probe microscopy; Metal; Chemistry; Nanotechnology; Corrosion; Electrochemistry; Metallurgy; Atomic force microscopy; Optics; Microstructure; Thermodynamics; Electrode; Environmental chemistry; Physics; Physical chemistry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008129035,0.0001892965,0.0002792241,0.0001124507,0.00007310585,0.0000205953,0.0001359238,0.0001334796,0.000004809821],"category_scores_gemma":[0.0005656931,0.000172688,0.00003824011,0.00008148544,0.00007915416,0.0003649692,0.000030976,0.0001122631,0.000001424346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002223574,"about_ca_system_score_gemma":0.00004079742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002459611,"about_ca_topic_score_gemma":0.000004716461,"domain_scores_codex":[0.9985875,0.00005754563,0.000726354,0.0001997511,0.0001285084,0.0003003057],"domain_scores_gemma":[0.9990713,0.0003412251,0.0002635776,0.0001732337,0.00009494469,0.00005573046],"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":[0.000015154,0.00002264117,0.0007644101,0.0002395121,0.000006726057,0.00000105058,0.0001654311,0.07894002,0.887072,0.00004830355,0.000003309126,0.03272142],"study_design_scores_gemma":[0.0003506479,0.00003732245,0.002497586,0.0004934517,0.0000126286,0.000003899045,0.0000261542,0.08757957,0.9080427,0.0004269101,0.0003286087,0.0002004938],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4824,0.000008532636,0.5166944,0.00001165105,0.0001504784,0.0002453219,0.00001644596,0.0001942948,0.0002788934],"genre_scores_gemma":[0.6915291,0.000009103052,0.3082752,0.000006535517,0.00006877839,0.00004362673,0.00001967837,0.00003804879,0.000009986924],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2091291,"threshold_uncertainty_score":0.7042013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01663695599028439,"score_gpt":0.2902804660085258,"score_spread":0.2736435100182414,"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."}}