PDF to PDF/A: Evaluation of Converter Software for Implementation in Digital Repository Workflow: Poster - iPRES 2012 - Digital Curation Institute, iSchool, Toronto
Bibliographic record
Abstract
PDF/A is a version of Portable Document Format backed by ISO standard that is designed for archiving and preservation of electronic documents.Many electronic documents exist in PDF format.Due to its popularity, the ability to convert an existing PDF into a conforming PDF/A file is as important, if not more, as being able to produce documents in PDF/A format in digital preservation.In recognition of this fact and encouraged by growing interest from its affiliates, the Florida Digital Archive (FDA) conducted an evaluation of several of the PDF to PDF/A converter applications, the result of which is reported in this paper.There is room for interpretation in the ISO standards concerning PDF/A, which can be manifest in the development of software.In selecting a PDF to PDF/A converter product, reliability of the outcome in terms of PDF/A compliance must be established along with functionality.The goal of this paper is not to rank or promote the software evaluated, but rather to document the FDA's evaluation process and present the results in such a way that they provide insight into challenges and potential drawbacks during similar evaluation or implementation.
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 | 0.024 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".