Forage Fish Interactions: a symposium on “Creating the tools for ecosystem-based management of marine resources”
Bibliographic record
Abstract
Abstract Peck, M. A., Neuenfeldt, S., Essington, T. E., Trenkel, V. M., Takasuka, A., Gislason, H., Dickey-Collas, M., Andersen, K. H., Ravn-Jonsen, L., Vestergaard, N., Kvamsdal, S., Gårdmark, A., Link, J., and Rice, J. Forage Fish Interactions: a symposium on “Creating the tools for ecosystem-based management of marine resources”. – ICES Journal of Marine Science, 71: . Forage fish (FF) have a unique position within marine foodwebs and the development of sustainable harvest strategies for FF will be a critical step in advancing and implementing the broader, ecosystem-based management of marine systems. In all, 70 scientists from 16 nations gathered for a symposium on 12–14 November 2012 that was designed to address three key questions regarding the effective management of FF and their ecosystems: (i) how do environmental factors and predator–prey interactions drive the productivity and distribution of FF stocks across ecosystems worldwide, (ii) what are the economic and ecological costs and benefits of different FF management strategies, and (iii) do commonalities exist across ecosystems in terms of the effective management of FF exploitation?
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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| 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".