{"id":"W2963199718","doi":"10.1107/s1600576719008744","title":"Diffracting-grain identification from electron backscatter diffraction maps during residual stress measurements: a comparison between the sin<sup>2</sup>ψ and cosα methods","year":2019,"lang":"en","type":"article","venue":"Journal of Applied Crystallography","topic":"Fatigue and fracture mechanics","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Pratt and Whitney Canada","keywords":"Electron backscatter diffraction; Detector; Diffraction; Debye; Materials science; Optics; Tilt (camera); Residual stress; Lattice constant; X-ray crystallography; Physics; Crystallography; Geometry; Condensed matter physics; Mathematics; Chemistry","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":[0.0009248993,0.0002732017,0.0004579715,0.0002980587,0.0001409983,0.0001722365,0.000281601,0.0001748758,0.00002548505],"category_scores_gemma":[0.00002255011,0.0002134003,0.0001558487,0.000298379,0.00003799324,0.0002974361,0.00003640385,0.0008411085,0.000004124086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007923206,"about_ca_system_score_gemma":0.00001262592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001137826,"about_ca_topic_score_gemma":0.00000635994,"domain_scores_codex":[0.9980589,0.0001349004,0.0007705464,0.0002323454,0.0004831242,0.0003201508],"domain_scores_gemma":[0.9986874,0.0002910282,0.0005097826,0.0003159469,0.00009523214,0.0001006397],"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":[0.0002778356,0.0001157369,0.04353552,0.0001792707,0.001283167,0.000002700558,0.002454594,0.01730781,0.9239596,0.00005529065,0.001021702,0.009806754],"study_design_scores_gemma":[0.005676273,0.0004307967,0.4556883,0.0004814627,0.001655351,0.00003730542,0.00804373,0.01114172,0.4958589,0.008988581,0.01037028,0.001627299],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9798314,0.0006130235,0.01844069,0.0001400043,0.0002962479,0.0003195546,0.00002095633,0.00005501093,0.0002831272],"genre_scores_gemma":[0.9967944,0.000094007,0.002609528,0.00004227609,0.0003528143,0.00001006349,0.00003981,0.00004993721,0.000007164057],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4281007,"threshold_uncertainty_score":0.8702214,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01439985942947816,"score_gpt":0.25860062994486,"score_spread":0.2442007705153819,"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."}}