{"id":"W2515092855","doi":"10.1107/s1600576716011924","title":"Quantitative characterization of the microstructure of heat-treated Zr-Excel by neutron line profile analysis","year":2016,"lang":"en","type":"article","venue":"Journal of Applied Crystallography","topic":"Nuclear Materials and Properties","field":"Materials Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Basic Energy Sciences; Natural Sciences and Engineering Research Council of Canada; Natural Resources Canada; University Network of Excellence in Nuclear Engineering; U.S. Department of Energy","keywords":"Microstructure; Materials science; Equiaxed crystals; Neutron diffraction; Ductility (Earth science); Dislocation; Martensite; Crystallography; Phase (matter); Alloy; Composite material; Crystal structure; Creep; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001228535,0.0001697659,0.0001576769,0.0003043857,0.0001178146,0.0002735195,0.000180819,0.0001529348,0.0008039802],"category_scores_gemma":[0.0001439753,0.000145204,0.0001249872,0.0002802559,0.0002056555,0.0001339812,0.00009872788,0.0001825714,0.0001656305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002580527,"about_ca_system_score_gemma":0.0001297358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001663651,"about_ca_topic_score_gemma":0.002696028,"domain_scores_codex":[0.9999101,0.000006217478,0.000004720122,0.00001896006,0.00004428416,0.00001563402],"domain_scores_gemma":[0.9999336,0.000008223106,0.00001701472,0.000006783012,0.00002720561,0.000007211456],"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":[0.00003042796,0.000003037414,0.0003996518,0.00001111252,0.000002405229,0.00001226644,0.000009544135,0.0001474676,0.9987057,0.00003212366,0.000004327068,0.0006418779],"study_design_scores_gemma":[0.000004495556,0.000087008,0.01756826,0.000003343421,0.00001157507,0.00007769503,0.00004345119,0.001559717,0.979812,0.00002424011,0.0008043787,0.00000399476],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9919658,0.0004468098,0.005993082,0.00001110141,0.000004957236,0.00001996968,0.0003553428,0.00005155603,0.001151391],"genre_scores_gemma":[0.9939393,0.0003526809,0.003743039,0.0000103059,0.000002225336,0.00001799548,0.0004869396,0.00002407205,0.001423364],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001663651,"threshold_uncertainty_score":0.003307879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006902379840400159,"score_gpt":0.2058117963897198,"score_spread":0.1989094165493196,"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."}}