Towards a replacement for Xeroradiography
Bibliographic record
Abstract
Xeroradiography has proven to be a powerful tool for the examination of archaeological finds and other cultural artefacts, but it is no longer readily available. Building on previous work by the authors, which outlined the basics of X‐ray image digitisation, it is shown here how the desirable characteristics of xeroradiographs (good resolution of detail, tolerance of scattered radiation, wide exposure latitude and edge enhancement) can be reproduced through the application of digital image processing (DIP) to good‐quality X‐ray film images. Radiographs with optimum resolution, image contrast and exposure latitude, and reduced levels of scatter, are gained through the careful selection of X‐ray energy and beam filtration, or with high‐energy X‐rays and the judicious use of lead screen intensifiers. The edge‐enhancement potential of some currently available computer software is explored. Details are provided of basic edge‐detection kernels and how they are applied to digitised images to provide edge enhancement.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".