Bibliographic record
Abstract
Several countries have taken steps to protect copyright data, some using judicial means and others implementing protection systems. Some Hydrographic Offices (HOs) feel that ENCs in particular, need to be protected from piracy and/or deliberate tampering. These issues are not unique to ENC (Electronic Navigational Chart) distribution but form a basic part of e-commerce infrastructure. A modern public key infrastructure system addresses the needs of data authentication, data security and non-repudiation. There are both technology and policy issues when one considers a protection system. The implementation of a tight security system along the entire distribution chain is necessary to make the system effective. Despite modern advances in encryption technology, there are some major impediments to achieving the goals of a security system. The complexities of key management and the acceptance of the user community are two of these. There is also the ethical issue of denying a ship access to ENCs once the license period has ended. The ship may have no other chart information on board. PRIMAR's Security System is currently gathering important operational experience in operating a protection system.
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 | 0.039 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.021 | 0.029 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.017 | 0.012 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".