Small pelagic fish: new frontiers in science and sustainable management
Bibliographic record
Abstract
Small pelagic fishes occupy an important trophic role in every global aquatic ecosystem, and many species are heavily exploited by fisheries, including some of the largest and most valuable capture fisheries in the world. In November 2022, a symposium on small pelagic fish titled “ Small Pelagic Fish: New Frontiers in Science and Sustainable Management” was cohosted by PICES, ICES, and FAO in Lisbon, Portugal. This special issue contains a collection of research manuscripts that explore approaches currently being used and developed to assess and manage small pelagic fishes. In particular, this issue covers topics on novel approaches to surveying small pelagic fishes, incorporating environmental covariates into management, management strategy evaluation, and aspects of the economics of small pelagic fisheries. The conclusions highlight the importance of new approaches that seek to enhance small pelagic fish surveys and ecosystem monitoring, incorporate that ecosystem information into management strategy evaluation, and predict the potential impacts of ecosystem changes on outcomes for economies and communities that rely on sustainable populations of small pelagic fishes.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".