{"id":"W4320168813","doi":"10.1016/j.jnucmat.2023.154285","title":"Plastic deformation of <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si4.svg\"><mml:mi>δ</mml:mi></mml:math>-zirconium hydride during micropillar compression","year":2023,"lang":"en","type":"article","venue":"Journal of Nuclear Materials","topic":"Nuclear Materials and Properties","field":"Materials Science","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"Canadian Nuclear Laboratories; University Network of Excellence in Nuclear Engineering; Natural Sciences and Engineering Research Council of Canada; Queen's University","keywords":"Zirconium; Materials science; Hydride; Zirconium alloy; Compression (physics); Transmission electron microscopy; Metallurgy; Composite material; Metal; Nanotechnology","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001076653,0.0002290067,0.0003644036,0.0001630696,0.0003427748,0.0004474878,0.0004864103,0.000248885,0.0009838004],"category_scores_gemma":[0.0001804667,0.0002152943,0.0001917296,0.000130067,0.0001707184,0.0007066869,0.0004158184,0.0001523042,0.002220084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002129949,"about_ca_system_score_gemma":0.00008667976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001036593,"about_ca_topic_score_gemma":0.000004808193,"domain_scores_codex":[0.9974852,0.0001399183,0.001101489,0.0002356637,0.0005937897,0.0004439786],"domain_scores_gemma":[0.9980617,0.0001002626,0.001234343,0.0003502595,0.0001120754,0.0001414214],"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":[0.0007232375,0.00004418908,0.000002956114,0.0006160122,0.00005069931,0.0000886788,0.001013687,0.0003588106,0.9215218,0.07453869,0.0009889284,0.00005228457],"study_design_scores_gemma":[0.0006730761,0.0004405234,0.000984618,0.000882253,0.0001027286,0.0004523524,0.0006098883,0.006096222,0.9859487,0.0001632069,0.003395076,0.0002513867],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996449,0.00007656605,0.000003964732,0.0001442687,0.002897029,0.00004678612,0.0000748014,0.00009206223,0.0002155075],"genre_scores_gemma":[0.9981242,0.0003085943,0.000690974,0.0001173361,0.0006067015,0.000007287549,0.00001594297,0.0001036211,0.0000252861],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07437547,"threshold_uncertainty_score":0.9999294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01824000734618883,"score_gpt":0.2288204180296032,"score_spread":0.2105804106834143,"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."}}