Data Mobilization Through the International Year of the Salmon Ocean Observing System
Bibliographic record
Abstract
Data mobilization—the process of making data available for appropriate re-use—remains a key barrier to effective salmon management. Data mobilization facilitates data sharing, discovery and reuse, and leads to increased citations, synthesis data products, meta-analyses and robust management-decision support-tools. The International Year of the Salmon (IYS) High Seas Expeditions in 2019, 2020, and 2022 presented a challenge and opportunity for data mobilization efforts due to the scale, volume, diversity of data, and number of nations involved. Here we demonstrate how we mobilized this salmon ocean ecology data—given the international scope of the project—through a natural alignment to the United Nations’ Global Ocean Observing System and Decade of Ocean Science for Sustainable Development. Under this framework datasets were catalogued using metadata records, assigned digital object identifiers, and published to a web accessible data catalogue (https://iys.hakai.org). Where possible, pre-existing community-developed and international data and metadata standards were adopted and data were published in global open-access repositories. Both the sociocultural and technical solutions implemented deepen the impact of the IYS and lay a foundation and clear path forward for salmon and oceanographic data producing institutions, commissions, foundations, or projects interested in contributing to international data-intensive salmon and oceanographic sciences.
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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 | 0.017 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".