Managing ocean information in the digital era – Events in Canada open questions about the role of marine science libraries
Bibliographic record
Abstract
Information is the foundation of evidence-based policies for effective marine environmental protection and conservation. In Canada, the cutback of marine science libraries introduces key questions about the role of such institutions and the management of ocean information in the digital age. How vital are such libraries in the mission of studying and protecting the oceans? What is the fate and value of the massive grey literature holdings, including archival materials, much of which is not in digital form but which often contains vital data? How important is this literature generally in the marine environmental sciences? Are we likely to forget the history of the marine pollution field if our digital focus eclipses the need for and access to comprehensive collections and skilled information specialists? This paper explores these and other questions against the backdrop of unprecedented changes in the federal libraries, marine environmental science and legislation in Canada.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.019 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 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".