Narrow-area Bragg-edge transmission of iron samples using superconducting neutron sensor
Bibliographic record
Abstract
Abstract This study investigates a current-biased kinetic inductance detector (CB-KID) performance by investigating Bragg-edge spectra from the restricted-area neuron transmission of materials. Iron samples with a size of 5 × 5 ×2 mm3 were used as typical test materials. The ergodic theorem was used to obtain a visible transmission spectrum so that a long-time averaging of a transmission spectrum can alternatively be evaluated using a space average of independently selected area spectra with the same ensemble size. The most visible edges were observed with a limited area sample of 0.43 mm2 using a minimum time bin of 25 μs in a time-of-flight (ToF) spectrum or a wavelength resolution of 0.0007 nm of each neutron pulse at beamline BL10 of the Japan Proton Accelerator Research Complex (J-PARC) center. The main Bragg edge of iron as a sum of random 100 ensembles (with an ensemble size of 3.1 × 2.3 μm2) thus obtained has a distinctive signal-to-noise ratio and can be fitted well with the Rietveld Imaging of Transmission Spectra (RITS) program with Miller indices. We consider that our CB-KID system is, in principle, able to analyze the Bragg edge of samples as small as 3.1 × 2.3 μm2.
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 | 0.000 | 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 | 0.000 | 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".