Properties of Quantum Beams and Their Applications
Bibliographic record
Abstract
A conceptual formulation of quantum beams and their basic properties are presented. The present status and outlook of their industrial applications are also discussed. Quantum beams are highly directional energy beams consisting of quantum-mechanical particles characterized by wave-particle duality. They are a concept developed out of need in industry, and, together with quantum mechanics developed during the turn of the century, have been applied to semiconductor and medical industries. The quantum beams can be classified by penetrating or ionizing power. X-rays and neutron beams are classified into those with high penetrating power, and the beams of alpha particles are classified into those with high ionizing power. Electron beams fall in between, giving rise to their unique intermediate property. Their chemical and biological effects are used in modifying the properties of materials or sterilizing food and beverage containers. Finally, we discuss the importance of developing further advanced accelerator technologies which can produce high-energy quantum beams, which will be necessary to chart our future research in yet unknown areas of science. In doing so, profit should not be the only goal; contribution to a sustainable society should be considered as well.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".