{"id":"W4411383044","doi":"10.1002/advs.202501907","title":"Secondary Electron Hyperspectral Imaging of Carbons: New Insights and Good Practice Guide","year":2025,"lang":"en","type":"article","venue":"Advanced Science","topic":"Electron and X-Ray Spectroscopy Techniques","field":"Materials Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Physical Laboratory; Engineering and Physical Sciences Research Council; Akademie Věd České Republiky; Ministerstvo Školství, Mládeže a Tělovýchovy; European Cooperation in Science and Technology; University of Oxford; Canada Foundation for Innovation; Faraday Institution","keywords":"Hyperspectral imaging; Nanotechnology; Environmental science; Materials science; Remote sensing; Geology","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":[],"consensus_categories":[],"category_scores_codex":[0.0005630694,0.0001646882,0.000225764,0.0002448872,0.0002452999,0.0001019937,0.000520447,0.00003167778,0.00002146374],"category_scores_gemma":[0.0004863895,0.0001511337,0.00003039758,0.0009005212,0.0006193129,0.001432145,0.0001439792,0.0002024618,0.00000374674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001803545,"about_ca_system_score_gemma":0.001121918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000228907,"about_ca_topic_score_gemma":0.00005133035,"domain_scores_codex":[0.9982028,0.00004611247,0.0002978392,0.0005830789,0.0003444251,0.0005257663],"domain_scores_gemma":[0.9990145,0.0001455838,0.0001575268,0.0003972345,0.0001707845,0.0001143794],"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":[0.00002906111,0.00002613411,0.00008556068,0.000009481177,0.000002009814,0.000003236893,0.0002053648,0.000005367807,0.9302275,0.06589413,0.0001669549,0.003345245],"study_design_scores_gemma":[0.000272581,0.0001506959,0.0004719269,0.00005105323,0.00001525267,0.00001942767,0.0002376615,0.00008042061,0.974256,0.0162777,0.00801493,0.0001523507],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8456659,0.01000266,0.009909421,0.002605651,0.0003790171,0.000448927,0.000003062489,0.0003470235,0.1306383],"genre_scores_gemma":[0.8988298,0.000380117,0.09895461,0.0008327625,0.00002628915,0.00001091349,5.438557e-7,0.00000957641,0.0009554315],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1296829,"threshold_uncertainty_score":0.6163054,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004662066534640777,"score_gpt":0.3046478861574741,"score_spread":0.2999858196228333,"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."}}