Bibliographic record
Abstract
Both physical sputtering and chemical erosion take place in tokamaks. Physical sputtering occurs for all elements for incident particle energies greater than an energy threshold. For carbon targets the threshold difference for the three hydrogen isotopes is relatively small. In the energy range of 100 to 3000 eV, the physical sputtering yields are similar for D and T, and the H yields are lower by about a factor of 2 to 3. Chemical erosion studies of graphite due to H+ and D+ impact also show evidence of some isotopic effect - with the deuterium yield being larger. The isotopic yield ratios (D-yield/H-yield) observed in almost all of the chemical erosion measurements, including ion beams, laboratory plasma devices, and tokamaks, lie between 1 and 2. The recently measured chemical erosion yields due to tritium ions also fall in this range. (The notable exceptions are the mass-loss studies at the Max-Planck Institut für Plasmaphysik in Garching, Germany, where for energies <100 eV, the isotopic yield ratio was seen to increase from 4 to 7 with decreasing energy.) A nominal value of 1.5 ± 0.5 is suggested as the most appropriate value for the D/H yield ratio. This is fully consistent with the square root of mass dependence proposed for the modeling of chemical erosion.
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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".