A Whole Ecosystem Approach to Marine Restoration: Optimal Strategies for Northern British Columbia
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.Marine ecosystem models (Ecopath with Ecosim) are used to evaluate potential wholeecosystem goals for restoration in northern British Columbia. A new methodology is introduced to achieve those goals. Optimal restorable biomass (ORB), the goal for restoration, is based on historic ecosystems. It is an ecosystem analogy to BMSY that maximizes stock production rates while trading off the relative biomass of interacting ecosystem components in order to satisfy harvest objectives. We use a non-linear search procedure to draft harvest plans that will restore the current ecosystem to an ORB state. The plans prescribe specific fleet-effort configurations that use fishing as a tool to selectively manipulate the ecosystem. Candidate restoration plans are evaluated in market terms using cost benefit analysis, and in non-market terms using new indices developed for this approach. Under some conditions, the return on the resource investment is comparable to bank interest. This work offers a new methodology to guide strategic ecosystem restoration using ecosystem models; first in evaluating restoration goals and then in developing optimal long-term fishing strategies to achieve those goals.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".