{"id":"W2040930857","doi":"10.4028/www.scientific.net/msf.768-769.227","title":"XRD&lt;sup&gt;2&lt;/sup&gt; Stress Measurement for Samples with Texture and Large Grains","year":2013,"lang":"en","type":"article","venue":"Materials science forum","topic":"X-ray Diffraction in Crystallography","field":"Materials Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bruker (Canada)","funders":"","keywords":"Materials science; Anisotropy; Diffraction; Texture (cosmology); Intensity (physics); Orientation (vector space); Grain size; Stress (linguistics); Statistics; X-ray crystallography; Optics; Condensed matter physics; Analytical Chemistry (journal); Composite material; Mathematics; Physics; Geometry; Image (mathematics); Artificial intelligence; Chemistry; Computer science","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":[0.001801823,0.001828913,0.002280337,0.002177373,0.001373298,0.001405859,0.001833986,0.001028931,0.07224351],"category_scores_gemma":[0.002949135,0.001694384,0.0004002315,0.004445776,0.001010705,0.001681452,0.0008283092,0.00278467,0.01915369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001172169,"about_ca_system_score_gemma":0.001099157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002234139,"about_ca_topic_score_gemma":0.007351819,"domain_scores_codex":[0.998604,0.0001229349,0.0001450811,0.0003855352,0.0005626527,0.0001798643],"domain_scores_gemma":[0.9974447,0.0004259582,0.0001411853,0.000746028,0.001114006,0.0001281717],"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":[0.0008452609,0.0001289349,0.001468824,0.001150496,0.00006018464,0.0006421061,0.0003334918,0.000532764,0.8874872,0.001847738,0.05902939,0.04647359],"study_design_scores_gemma":[0.0001634601,0.0001507857,0.007930099,0.00007162713,0.00008602784,0.0006621973,0.0003168899,0.003807936,0.8462283,0.0007369675,0.1397834,0.00006227317],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3207024,0.004452568,0.3425373,0.004194263,0.004520629,0.002873054,0.09912317,0.03368611,0.1879104],"genre_scores_gemma":[0.3711919,0.00843681,0.36723,0.001763759,0.0004604576,0.004440232,0.1057254,0.03064733,0.1101041],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.07224351,"threshold_uncertainty_score":0.2416786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01934453713597757,"score_gpt":0.2409026716255339,"score_spread":0.2215581344895564,"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."}}