Modelling stock dynamics in the southern Benguela ecosystem for the period 1978–2002
Bibliographic record
Abstract
An ecosystem model of the southern Benguela was fitted to available time-series data for the period 1978–2002, to explore how changes in target fish populations in this ecosystem can be attributed to feeding interaction terms and population control patterns, the impact of fishing, and environmental forcing. Fishing patterns were estimated to explain only 2–3% of the variability in the time-series, whereas an estimated productivity forcing pattern applied to phytoplankton explained 4–12% of the variance represented by the sum of squares. Model settings describing prey vulnerability to their predators could explain around 40% of the variability in the time-series. Modelled stock dynamics in the southern Benguela ecosystem more closely represent observed timeseries when wasp-waist control by small pelagic fish is simulated. Overall, model simulations suggest that almost half the variance in the time-series can be explained based on a combination of fishing, vulnerability settings and productivity patterns. Variation in mortalities and prey preferences over time, as well as model fits in relation to available effort series, are discussed. The study advances a model with improved parameterization and credibility to assist with an ecosystem approach to South African fisheries management.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".