Modelling stock dynamics in the southern Benguela ecosystem for the period 1978–2002
Bibliographic record
Abstract
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. Keywords: ECOSYSTEM MODELLINGENVIRONMENTAL FORCINGFISHING PATTERNSSOUTHERN BENGUELASTOCK DYNAMICSWASP-WAIST
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 teacher head, 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".