Energy-resolved neutron imaging with high spatial resolution using a superconducting delay-line kinetic inductance detector
Bibliographic record
Abstract
Abstract Neutron imaging is one of the key technologies for non-destructive transmission testing. Recent progress in the development of intensive neutron sources allows us to perform energy-resolved neutron imaging with high spatial resolution. Substantial efforts have been devoted to developing a high spatial and temporal resolution neutron imager. We have been developing a neutron imager aiming at conducting high spatial and temporal resolution imaging based on a delay-line neutron detector, called the current-biased kinetic-inductance detector, with a conversion layer 10 B. The detector allowed us to obtain a neutron transmission image with four signal readout lines. Herein, we expanded the sensor active area, and improved the spatial resolution of the detector. We examined the capability of high spatial resolution neutron imaging over the sensor active area of 15 × 15 mm 2 for various samples, including biological and metal ones. We also demonstrated an energy-resolved neutron image in which stainless-steel specimens were discriminating of other specimens with the aid of the Bragg edge transmission.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".