Development of a Time-of-Flight Neutron Emission Spectrometer for General Fusion’s Fusion Demonstration Program
Bibliographic record
Abstract
General Fusion is developing a Magnetized Target Fusion approach that involves compressing an initial magnetically confined plasma inside a cavity formed in liquid metal. General Fusion has launched a Fusion Demonstration Program to design and construct a large scale, integrated prototype, being built at the Culham Centre for Fusion Energy in the United Kingdom. It is proposed to implement a highly segmented, time-of-flight Neutron Emission Spectrometer to measure the energy distribution of neutrons emitted nominally at 2.45 MeV from deuterium-deuterium fusion. This distribution will be used to extract the ion plasma temperature at the late stages of compression. An incident neutron flux of up to one billion neutrons per second is expected at the spectrometer, resulting in very demanding performance criteria. It is vital that the Fusion Demonstration Program’s prototype machine have a diagnostic system capable of measuring plasma ion temperatures of 10 keV (116 million degrees Celsius) to evaluate the success of a compression and the overall performance. Simulations modeling plasma compression, neutron interactions with the prototype’s apparatus and spectrometer, as well as the development of a prototype time-of-flight spectrometer in partnership with TRIUMF and Simon Fraser University, will be discussed.
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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 | 0.003 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".