{"id":"W4311142311","doi":"10.1016/j.dib.2022.108808","title":"SEM and TEM data of nuclear graphite and glassy carbon microstructures","year":2022,"lang":"en","type":"article","venue":"Data in Brief","topic":"Graphite, nuclear technology, radiation studies","field":"Materials Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Nuclear Laboratories","funders":"Office of Nuclear Energy; Oak Ridge National Laboratory; UT-Battelle; Engineering and Physical Sciences Research Council; U.S. Department of Energy","keywords":"Graphite; Nuclear graphite; Microstructure; Materials science; Transmission electron microscopy; Scanning electron microscope; Carbon fibers; Composite material; Micrograph; Phase (matter); Nanotechnology; Chemistry; Composite number; Organic chemistry","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":[0.0002296474,0.0002054605,0.0001289079,0.001848606,0.0006028487,0.0002365554,0.0001620911,0.0002911063,0.006932871],"category_scores_gemma":[0.0003131978,0.0001333785,0.0001384099,0.001481361,0.0002556342,0.0002155327,0.0001493618,0.0003317929,0.0008078276],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001963367,"about_ca_system_score_gemma":0.0002783559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002713993,"about_ca_topic_score_gemma":0.01034172,"domain_scores_codex":[0.9998077,0.000007985811,0.0000164032,0.00002809652,0.0001231855,0.00001654582],"domain_scores_gemma":[0.9996911,0.00004707134,0.00003671511,0.00004489266,0.0001507092,0.00002952191],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008515469,0.00001905415,0.003297144,0.0002120878,0.00001120319,0.0005969543,0.0002122116,0.0005261739,0.9748912,0.0008608468,0.001768039,0.01751995],"study_design_scores_gemma":[0.00000947272,0.0001807483,0.1606929,0.00007048742,0.00004203268,0.002927431,0.0007963815,0.002416447,0.7761697,0.0005264382,0.05612207,0.00004594243],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.9150121,0.001854941,0.02334756,0.0003355413,0.0001807532,0.000285804,0.02070859,0.0009551534,0.03731956],"genre_scores_gemma":[0.914256,0.002385546,0.04587673,0.0001879909,0.00004874492,0.0001569279,0.01423565,0.0002693313,0.0225832],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.006932871,"threshold_uncertainty_score":0.02319282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02832524488894098,"score_gpt":0.2598892222414848,"score_spread":0.2315639773525438,"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."}}