The optimal optical readout for the x‐ray light valve—Document scanners
Bibliographic record
Abstract
The x-ray light valve (XLV) is a novel, potentially low-cost, x-ray detector that converts an x-ray exposure into an optical image stored in a liquid crystal cell. This optical image is then transferred from the liquid crystal cell to a computer through an optical-to-digital imaging readout system. Previously, CCD-based cameras were used for the optical readout, but recently it was proposed that an inexpensive optical scanner, such as an office document scanner, is a better match to the optical properties of the XLV. A methodology for characterizing a document scanner's ability to produce medical quality images from the XLV is outlined and tested on a particular scanner (Canon LiDE 30). This scanner was shown to have key characteristics of a medical device-a linear response, dynamic range sufficient for chest radiography (although not mammography) in a single pass, and an MTF and NPS that exceed the requirements for all medical applications of the scanner. This combination of criteria shows that a document scanner can be used as a digitization method for the XLV.
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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".