Bibliographic record
Abstract
This thesis discusses the development, benchmarking and applications of activation dose analysis methods for fusion devices. The development and code logic of the Mesh Coupled Rigorous 2 Step (MCR2S) system is discussed. Following the development of the code, appropriate benchmarking studies were performed on the Frascati neutron generator, and revealed that the code was able to predict shutdown gamma ray doserates to within ±3% of experimentally determined values, for decay times between 3×105 and 107 seconds. The development of the Ion Cyclotron Resonance Heater (ICRH) with regards to neutronics was discussed. The ICRH went through a number of design stages and shutdown gamma ray dose rates were determined for each stage. It was determined that of all the designs analysed only one of them, the first concept design for the internally matched design did not meet the shutdown dose criteria. This was due to a flaw in the system design, brought about by a lack of consideration towards nuclear design. The ITER Light Imaging Detection and Ranging (LIDAR) system was subjected to a full shutdown nuclear analysis. It was found that the design of the LIDAR system supplied did not meet the ITER required shutdown gamma ray dose rate limit of 100 µSvhr−1, however use of the MCR2S system highlighted the components that contributed most to the shutdown gamma ray dose rate and were shown to be the mirror holder and the laser beam pipe. Future designs should include additional shielding around the beam pipe.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".