Incorporating environmental variability in stock assessment: predicting recruitment, spawner biomass, and landings of sprat (Sprattus sprattus) in the Baltic Sea
Bibliographic record
Abstract
Temperature has a significant positive impact on recruitment of sprat, Sprattus sprattus, in the Baltic Sea. Here we evaluate whether an existing recruitment model for the year classes 1973–1999 can forecast recruitment for five new year classes. The coefficient of variation (CV) of predictions was 5%, and four of five new year classes were within 95% confidence limits of predictions made by the earlier model. We then assimilated climatic, oceanographic, and recruitment linkages and their uncertainty into the standard International Council for the Exploration of the Sea (ICES) assessment procedure to predict key advisory-related variables such as spawning stock biomass (SSB) and landings. These linkages enable a forecast of recruitment earlier than the annual assessment meeting. Forecasts made using the North Atlantic Oscillation to predict the 2006 year class showed that spawner biomass would be 15% lower than spawner biomass calculated using the ICES standard methodology. The difference in perception of future biomass does not affect the advice for the stock because the spawning stock biomass is greater than the critical biomass limit (SSB > BPA). However, when this is not the case or when it is desirable to broaden the ecosystem basis for fisheries management, incorporation of knowledge of recruitment processes may be beneficial.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".