{"id":"W2746940293","doi":"10.3390/app7080854","title":"Irradiation Induced Defect Clustering in Zircaloy-2","year":2017,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Nuclear Materials and Properties","field":"Materials Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Argonne National Laboratory; Division of Materials Research; Office of Science; Natural Sciences and Engineering Research Council of Canada; Mitacs; University Network of Excellence in Nuclear Engineering; U.S. Department of Energy","keywords":"Irradiation; Materials science; Nucleation; Dislocation; Crystallographic defect; Yield (engineering); Vacancy defect; Ion; Microstructure; Zirconium alloy; Crystallography; Analytical Chemistry (journal); Metallurgy; Composite material; Thermodynamics; Zirconium; Chemistry; Nuclear physics","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":[0.00006740641,0.0001795153,0.0002305592,0.0001563001,0.0001691838,0.0002702487,0.0003615821,0.0001841494,0.001283554],"category_scores_gemma":[0.0001630763,0.0001900401,0.0001713386,0.0001793245,0.0001692186,0.0001507809,0.0002012809,0.0001791031,0.000112985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000615205,"about_ca_system_score_gemma":0.0001796239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00332527,"about_ca_topic_score_gemma":0.003382589,"domain_scores_codex":[0.9999173,0.000004090076,0.000004654089,0.00002483703,0.00002899849,0.00002020584],"domain_scores_gemma":[0.9999385,0.00001147756,0.0000227827,0.000005431261,0.00001404426,0.000007789261],"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":[0.00009572269,0.00001051582,0.001073976,0.00005245956,0.00001038658,0.00008846539,0.0000445151,0.001847379,0.9959854,0.00008733672,0.0000456814,0.0006581852],"study_design_scores_gemma":[0.00002543353,0.0003223181,0.02673083,0.000006028549,0.00001780941,0.000128203,0.00009522418,0.01397403,0.9575968,0.00005212393,0.001032052,0.00001909896],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9993489,0.0001029976,0.0001813642,0.000008249645,0.000001823715,0.000003459708,0.00009170312,0.00002687167,0.0002346575],"genre_scores_gemma":[0.9992499,0.00004504113,0.0002219996,0.000003329711,6.458085e-7,0.000005946906,0.0001795063,0.00001137664,0.0002823559],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00332527,"threshold_uncertainty_score":0.006611884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06310287733346553,"score_gpt":0.2854853674594559,"score_spread":0.2223824901259904,"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."}}