Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Dust production and accumulation present potential safety and operational issues for the ITER. Dust diagnostics can be divided into two groups: diagnostics of dust on surfaces and diagnostics of dust in plasma. Diagnostics from both groups are employed in contemporary tokamaks; new diagnostics suitable for ITER are also being developed and tested. Dust accumulation in ITER is likely to occur in hidden areas, e.g., between tiles and under divertor baffles. A novel electrostatic dust detector for monitoring dust in these regions has been developed and tested at PPPL. In the DIII-D tokamak dust diagnostics include Mie scattering from Nd:YAG lasers, visible imaging, and spectroscopy. Laser scattering is able to resolve particles between 0.16 and 1.6 microm in diameter; using these data the total dust content in the edge plasmas and trends in the dust production rates within this size range have been established. Individual dust particles are observed by visible imaging using fast framing cameras, detecting dust particles of a few microns in diameter and larger. Dust velocities and trajectories can be determined in two-dimension with a single camera or three-dimension using multiple cameras, but determination of particle size is challenging. In order to calibrate diagnostics and benchmark dust dynamics modeling, precharacterized carbon dust has been injected into the lower divertor of DIII-D. Injected dust is seen by cameras, and spectroscopic diagnostics observe an increase in carbon line (CI, CII, C(2) dimer) and thermal continuum emissions from the injected dust. The latter observation can be used in the design of novel dust survey diagnostics.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.061 | 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 it