Physical forcing and the dynamics of the pelagic ecosystem in the eastern tropical Pacific: simulations with ENSO-scale and global-warming climate drivers
Bibliographic record
Abstract
We used a model of the pelagic ecosystem in the eastern tropical Pacific Ocean to explore how climate variation at El Niño Southern Oscillation (ENSO) scales might affect animals at middle and upper trophic levels. We developed two physical-forcing scenarios: (1) physical effects on phytoplankton biomass and (2) simultaneous physical effects on phytoplankton biomass and predator recruitment. We simulated the effects of climate-anomaly pulses, climate cycles, and global warming. Pulses caused oscillations to propagate through the ecosystem; cycles affected the shapes of these oscillations; and warming caused trends. We concluded that biomass trajectories of single populations at middle and upper trophic levels cannot be used to detect bottom-up effects, that direct physical effects on predator recruitment can be the dominant source of interannual variability in pelagic ecosystems, that such direct effects may dampen top-down control by fisheries, and that predictions about the effects of climate change may be misleading if fishing mortality is not considered. Predictions from ecosystem models are sensitive to the relative strengths of indirect and direct physical effects on middle and upper trophic levels.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".