Bibliographic record
Abstract
A Monte Carlo simulation of tritium β particle motion in a matrix material indicates that the probability of escape through the material surface is greater than 50% for near-surface tritium, but drops quite rapidly with increasing depth. Beyond an areal density depth of 2×10−5 g/cm2, the decline is close to exponential, with an e-folding length of approximately 3×10−5 g/cm2, the exact value varying slightly with the material. The sensitivity of the tritium imaging technique will decrease at a similar rate with increasing depth of tritium. Experimentally, the image intensity of a tritium-implanted graphite specimen was observed to decrease exponentially with coating thickness as an aluminum layer was evaporated onto its surface, with an exponent that was within 30% of the predicted value. The Monte Carlo simulation also indicated that the limit of lateral resolution of the tritium imaging technique will be slightly less than the depth of the tritium, subject always to Recknagel’s limit of resolution due to “chromatic aberration” of the electron optics, which is of the order of 200 nm. If tritium is uniformly distributed through the material, surface tritium so dominates image formation that Recknagel’s limit inevitably applies.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".