Data for “Secondary electron hyperspectral imaging of carbons: New insights and good practice guide”
Bibliographic record
Abstract
This repository contains the data package for the research paper titled "Secondary electron hyperspectral imaging of carbons: New insights and good practice guide".Contact: SM3 (SEE MORE MAKE MORE) project PI, Professor Cornelia Rodenburg, c.rodenburg@sheffield.ac.uk.Acknowledgements:JFN and SC acknowledge support from the Faraday Institution through project FutureCat (FIRG017).JFN acknowledges support from the Faraday Institution through the studentship (FITG028-B) and thanks Arron Bird for providing CVD carbon reference materials.The authors acknowledge EPSRC funding through See More Make More: EP/V012762/1, EP/V011995/1, EP/V012037/1.The authors acknowledge: use of characterisation facilities within the David Cockayne Centre for Electron Microscopy (DCCEM), Department of Materials, University of Oxford, alongside financial support provided by the Henry Royce Institute (Grant ref EP/R010145/1); use of facilities within the Loughborough Materials Characterisation Centre and for access to the Helios PFIB, funded by the EPSRC grant EP/P030599/1; access to the Helios Nanolab 650 in the Centre for High-Throughput Phenogenomics at the University of British Columbia, a facility supported by the Canada Foundation for Innovation, British Columbia Knowledge Development Foundation, and the UBC Faculty of Dentistry; Electron microscopy and analysis was performed in the Sorby Centre for Electron Microscopy at the University of Sheffield.ZP, FM and TM acknowledge support from The Czech Academy of Sciences (project RVO:68081731 and Strategy AV21, Breakthrough future technologies), CF Electron microscopy and Raman spectroscopy (ISI CAS) supported by the Czech-BioImaging large RI project (LM2023050 funded by MEYS CR) for access to the Helios G4 HP (courtesy Thermo Fisher Scientific Brno).AK, AT, SC and CR acknowledge the EPSRC grant EP/V007696/1, "Near-Field Optical Spectroscopy Centre at Sheffield, NOSC”.CR, NTHF and FM acknowledge discussions enabled by FIT4NANO (CA19140) through FIT4NANO workshops.The authors acknowledge Dr Benjamen Reed (National Physical Laboratory, U.K.) for the acquisition and analysis of the XPS data provided in this report, and for discussions and comments. The authors further acknowledge Dr Vivian Tong (National Physical Laboratory, U.K) for providing comments. These activities were supported by the National Measurement System of the UK Department of Science, Innovation and Technology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.011 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".