Evaluation of the performance of erasable marker pen ink for the development of indentations on documents upon surface charging by electrostatic detection device
Bibliographic record
Abstract
Indented impressions can be left on the surface beneath a document when it is written on. In the absence of this document, electrostatic detection devices can be used to reveal the underneath previously written information. However, there are instances where the toner used to develop these indentations has to be substituted with alternative application, under unexpected circumstances, such as the supply chain disruption during the ongoing global pandemic. This study aimed to verify the use of erasable marker pen ink as an alternative application for the development of indentations. The procedure was optimized and evaluated, and its performance in deciphering indented impressions from 11 different underlying surfaces was compared to a conventional electrostatic detection device that applied toner to develop indentation. Electrostatic device with toner application using cascade developer method has successfully developed indented impressions from all surfaces, except for the coated glossy paper. In contrast, the application of erasable marker pens revealed indentation successfully from not only the coated glossy paper but also six other common writing surfaces. While the toner is a reliable application for deciphering indentations, the application of erasable marker ink pen can be used in the event when toners are unavailable but also on surfaces such as glossy paper, where application of toners to develop indentation may not provide satisfactory results.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".