{"id":"W4388871838","doi":"10.1016/j.matchar.2023.113498","title":"Investigation of fine-scale dislocation distributions at complex geometrical structures by using HR-EBSD and a comparison with conventional EBSD","year":2023,"lang":"en","type":"article","venue":"Materials Characterization","topic":"Nuclear Materials and Properties","field":"Materials Science","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Queen's University; University Network of Excellence in Nuclear Engineering; McMaster University","keywords":"Electron backscatter diffraction; Misorientation; Materials science; Dislocation; Crystallite; Diffraction; Crystallography; Deformation (meteorology); Composite material; Optics; Metallurgy; Microstructure; Grain boundary","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":[0.0003604861,0.0003714703,0.0002994816,0.001155317,0.0002764754,0.0006332268,0.0004619179,0.0004845201,0.00246244],"category_scores_gemma":[0.0007741721,0.0003249826,0.0001776491,0.000695354,0.000576328,0.0008142235,0.000273175,0.0003227939,0.0003065144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002619554,"about_ca_system_score_gemma":0.0001573735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009036254,"about_ca_topic_score_gemma":0.001491196,"domain_scores_codex":[0.9998065,0.00001138671,0.00001430864,0.00005859849,0.00008417704,0.0000250733],"domain_scores_gemma":[0.9992694,0.0002193913,0.00006718245,0.0001733444,0.0002444072,0.00002622974],"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":[0.00008937759,0.0000695632,0.002714819,0.0001760679,0.00001815225,0.0001952247,0.0001769486,0.002237624,0.9817774,0.000974051,0.0001031151,0.01146766],"study_design_scores_gemma":[0.00003059331,0.0001940426,0.05249353,0.00002656307,0.00005004654,0.001422261,0.0003669619,0.02988671,0.9100123,0.0009466727,0.004529459,0.00004086429],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8926002,0.001502855,0.09801974,0.00007074569,0.00004452396,0.0001128565,0.0006682149,0.0006714475,0.006309507],"genre_scores_gemma":[0.9500616,0.0006271135,0.04680067,0.00002494382,0.00001333186,0.00004176375,0.0002778837,0.00007818712,0.002074535],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00246244,"threshold_uncertainty_score":0.00823772,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05012067397836183,"score_gpt":0.2593153886540204,"score_spread":0.2091947146756585,"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."}}