Fishing on ecosystems: the interplay of fishing and predation in NewfoundlandLabrador
Bibliographic record
Abstract
In the early 1990s, Atlantic cod, a major component of the NewfoundlandLabrador ecosystem, suffered a stock collapse, and other groundfish stocks such as American plaice and yellowtail flounder seriously declined. This paper explores whether the relative effects of predation and fishing alone can account for these ecosystem changes. The NewfoundlandLabrador ecosystem was first modelled with a mass balance model for a time period in the mid-1980s when the groundfish biomass was relatively stable. This provided the starting point for simulations using a trophodynamic simulation model, Ecosim. A series of simulations were run, under different assumptions about energy control, to address the larger question "can the effects of fishing and predation account for the changes observed in the ecosystem?" The collapse and nonrecovery of cod was replicated, assuming top-down energy control. Other control assumptions were less successful. While groundfish stocks collapsed, seal populations and invertebrates such as shrimp and snow crab increased in abundance. The model predicted these increases, while a simulated increase in harp seals further repressed the recovery rate of cod. It was concluded that these results are consistent with the hypothesis that the collapse of cod was caused by excess fishing and that cod recovery is retarded by harp seals.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".