Bibliographic record
Abstract
Polarized neutrons scattering is an important technology for characterizing magnetic and other materials. Polarized helium three (P-3He) is a novel technology for creating polarized beams and, perhaps more importantly, for the analysis of polarization in highly divergent scattered beams. Analysis of scattered beams requires specialized targets with complex geometries to ensure accurate results. Special materials and handling procedures are required to give the targets a long useful lifetime. In most cases, the targets must be shielded from stray magnetic fields from nearby equipment. SRL has developed and demonstrated hybrid targets made from glass and aluminum. We have also developed and calibrated a low-field NMR system for measuring polarization lifetimes. We have demonstrated that our low-field system is able to measure NMR signals in the presence of conducting (metallic) cell elements. We have also demonstrated a non-magnetic valve that can be used to seal the cells. We feel that these accomplishments in Phase I are sufficient to ensure a successful Phase II program. The commercial market for this technology is solid. There are over nine neutron scattering centers in the US and Canada and over 22 abroad. Currently, the US plans to build a new $1.4B scattering facility called the Spallation Neutron Source (SNS). The technology developed in this project will allow SRL to supply targets to both existing and future facilities. SRL is also involved with the application of P-3He to medical imaging.
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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 | 0.001 |
| 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) | 0.004 | 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".