Bibliographic record
Abstract
[Jon Hykawy] explained how the pebble bed modular reactor (PBMR) design, which uses fuel 'pebbles' composed of a highly pure, nuclear grade graphite matrix hosting ceramic-coated fuel pellets as well as a graphite-lined reactor core, has been pursued for decades as a safer and more efficient alternative to conventional models. Hykawy pointed to calculations by the Massachusetts Institute of Technology (MIT), which suggest that an 110 MWe PBMR, based on MIT's own design, requires 80 tonnes of graphite in the form of uranium-flecked pebbles; 28 tonnes of graphite as pure pebbles for the core neutron reflector; and 426 tonnes of solid graphite in the reactor's outer reflector shield. So the overall winner for PBMR is probably going to be synthetic graphite, he said, but added that two Canadian natural graphite companies - Zenyatta Ventures, which owns the Albany graphite deposit in Ontario and Canada Carbon, which owns the Miller property in Quebec - have demonstrated the ability to produce the required purity, using a caustic bake process on the hydrothermal lump/vein-type ores yielded by the two deposits.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.004 |
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".