Simplifying ARK ID management for persistent access to digital objects
Bibliographic record
Abstract
This article will provide a brief overview of considerations made by the UTSC Library in selecting a persistent identifier scheme for digital collections in a mid-sized Canadian library. ARKs were selected for their early support of digital object management, the low-cost, decentralized capabilities of the ARK system, and the usefulness of ARK URLs during system migration projects. In the absence of a subscription to a centralized resolver service for ARKs, the UTSC Library Digital Scholarship Unit built an open source PHP-based application for minting, binding, managing, and tracking ARK IDs. This article will introduce the application's architecture and affordances, which may be useful to others in the library community with similar use cases, as well as the approach to using ARKs planned for an Islandora 2.x system.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".