Evaluating Marine Ecosystem Restoration Goals for Northern British Columbia
Bibliographic record
Abstract
Using the Ecopath with Ecosim (EwE) framework, we employ historical models of northern British Columbia marine ecosystems corresponding roughly to the years 1750, 1900, 1950, and 2000 to assess them as possible restoration goals. We use a policy optimization routine to identify fishing patterns that maximize economic, social, and ecological benefi ts from the restored historic systems. The ecosystem models are subjected to simulated harvest under optimal fishing plans, and the most beneficial scenarios are identified through various economic, social, and ecological indices. Knowing what a restored system may be worth to stakeholders could help us to justify the costs of whole ecosystem restoration. The 1750 ecosystem emerges as the most desirable restoration goal, owing to its large biomass of valuable target species. It is able to deliver the greatest sustainable benefits in terms of fisheries rent and employment, while sacrificing less biodiversity per dollar harvested. The 1900 period is slightly less attractive in all regards. The 2000 system offers superior benefits to 1950 in terms of potential rent and jobs, though not biodiversity. Ecosystem models have data deficiencies, and parameter uncertainty can compromise optimal harvest predictions. The problem is amplified when we reach into the past, where data for even the most visible species may come from anecdotal accounts. EwE provides a capacity to deal with data uncertainty; here we test ecosystem effects of our optimal harvest policies given unsure initial biomass estimates. Poor quality input data and/or heavy exploitation rates lead to large variations in the predicted structure of the ecosystem following optimal harvests.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".