Reconstructing ecosystem dynamics in the central Pacific Ocean, 19521998. II. A preliminary assessment of the trophic impacts of fishing and effects on tuna dynamics
Bibliographic record
Abstract
Pelagic fisheries in the Pacific Ocean target both large (Thunnus spp.) and small tunas (juveniles of Thunnus spp; Katsuwonus pelamis) but also take billfishes (Xiphias gladius, Makaira spp., Tetrapturus spp., Istiophorus platypterus) and sharks (Prionace glauca, Alopias superciliosus, Isurus oxyrinchus, Carcharhinus longimanus, Galeocerdo cuvieri) as bycatch. We developed a multispecies model using the Ecopath with Ecosim software that incorporated time-series estimates of biomass, fishing mortality, and bycatch rates (19521998) to evaluate the relative contributions of fishing and trophic impacts on tuna dynamics in the central Pacific (0°N to 40°N and 130°E to 150°W). The Ecosim model reproduced the observed trends in abundance indices and biomass estimates for most large tunas and billfishes. A decline in predation mortality owing to depletion of large predators was greatest for small yellowfin tuna and could possibly account for apparent increases in biomass. For other tunas, however, predicted changes in predation mortality rates were small (small bigeye) or were overwhelmed by much larger increases in fishing mortality (skipjack and small albacore). Limited evidence of trophic impacts associated with declining apex predator abundance likely results from the difficulties of applying detailed trophic models to open ocean systems in which ecological and fishery data uncertainties are large.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".