Educational Applications of Pyroelectric Acceleration
Bibliographic record
Abstract
In order to graduate the United States Military Academy (USMA) with an undergraduate degree in nuclear engineering or physics, each cadet must complete a capstone project.They also have the option of completing an independent study to graduate with honors or further their future educational opportunities.The Nuclear Science and Engineering Research Center (NSERC), a Defense Threat Reduction Agency (DTRA) office, sponsors these projects, providing resources and expertise in the fields on nuclear engineering and physics.One such resource is an experimental pyroelectric crystal accelerator to provide hands-on research and experimental experience for cadets.They can design their own experiments with the inexpensive tabletop accelerator that exists at USMA.The accelerator heats pyroelectric crystals, which creates a potential that ionizes and accelerates gas ions to energies upwards of ~150 keV.Currently, cadets working on the project are adding deuterium-deuterium (D-D) gas to create neutrons through fusion, creating a compact neutron source.This provides cadets with the opportunity to begin to live their learning and foster the development of critical thinking, as well as problem solving skills.This especially holds true because cadets can be innovative and determine their own experimental procedures and future research goals.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".