The identification of tattoo designs under cover-up tattoos using digital infrared photography
Bibliographic record
Abstract
This paper looks at digital infrared photography as a technique for identifying primary tattoos even if they have been covered up with additional tattoos. The study's goal was to look at a sufficient number of cover-up tattoos using infrared photography to enable the technique to be used more widely, and to attempt to elucidate the reasons for successful and unsuccessful infrared photography of primary tattoos through cover-up tattoos. Thirty-six tattoos were photographed in infrared along with colour control records. The results showed that primary tattoos could be visualized to some extent in 55.6 % of the cover-up tattoos and very well in 38.9%, this still left some 44.4% where the design of the primary tattoo could not be seen. The extent of visibility of underlying designs was found to depend on the ink colour, ink density and the extent to which the tattooist covered or incorporated the existing tattoo into the new design.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".