Salmon Data Mobilization
Bibliographic record
Abstract
Despite substantial research and conservation efforts, many salmon populations are in decline. Globally, salmon research is not delivering effective decision support products to help managers apply research insights as informed management actions. Data Mobilization (DM) is a key step towards building the wider evidence base required to deliver accountable, reliable, and usable scientific advice to managers. Best practices for DM are being adopted throughout the scientific community but have not permeated deeply into the culture of salmon research and conservation. To address this, we present a strategy for Salmon Data Mobilization (SDM). This strategy defines three spheres of agencies and practitioners that must interact to advance SDM: (1) authoritative bodies that can create policies to support SDM uptake; (2) agencies that can promote, fund, and implement those policies; and (3) the broad salmon community of practice that can support uptake of SDM within focused interest groups. We sketch a future for SDM and propose functional changes required to improve it throughout the community.
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 | 0.115 | 0.233 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.006 | 0.036 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.116 | 0.065 |
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".