Determining Concentration Limits for Boron Quantification Using EELS and for Energy-Filtered Imaging
Bibliographic record
Abstract
Abstract Electron energy-loss spectrometry and energy-filtered imaging allow the possibility of detecting, quantifying and mapping of boron. Boron spatial distribution in biological tissue is of particular interest for boron neutron capture therapy (BNCT) for cancer. We have studied the limits of boron quantification and mapping using electron energy-loss spectroscopy and energy-filtered imaging. To evaluate the concentration limits for boron mapping and quantification three types of specimens were used. First, a uniform boron layer of well known thickness deposited onto of 50 nm-thick carbon film was used to determine the limits for boron quantification. Second, samples for boron mapping with non-uniform boron distribution were prepared by electron-beam evaporation of boron onto a shadow-masked 50 nm-thick carbon film. Third, tobacco mosaic virus (TMV) in a water solution of boronophenylalanine fructrose (BPA-F) was deposited onto a 2 nm—thick carbon film.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".