What drives dynamics in the Gulf of Alaska? Integrating hypotheses of species, fishing, and climate relationships using ecosystem modeling
Bibliographic record
Abstract
We use a dynamic ecosystem model to evaluate the relative effects of fishing history, climate change, and predator–prey interactions in determining biomass trajectories for 12 species groups ranging from marine mammals through commercially exploited fish and invertebrates in the Gulf of Alaska (GOA). Ecosystem model fits under six alternative hypotheses relating fishing, climate, and predation were evaluated. Fishing alone explained few GOA biomass trajectories; it was necessary to both estimate specific predator–prey relationships and provide some mechanism for increased production. No single control hypothesis explained all species dynamics simultaneously, suggesting that in the GOA, there is no single main driver of the ecosystem. Furthermore, the alternative control hypotheses implied contrasting sets of predator–prey relationships (estimated functional response parameters). Therefore, a single set of “best fit” parameters for a given control hypothesis is unlikely to be useful in forecasting. Future modeling efforts supporting ecosystem-based fishery management could retain multiple working models to accommodate complex forcing (fishing, keystone species production, and environmental) differentially affecting components of the ecosystem.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".