RETRACTED: Ecosystem-based management approaches for watershed conservation and geosustainability
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
When contributors' goals and legislative and political structures vary, as they often do in the case of worldwide fish populations, it becomes more challenging to implement ethical fishing tactics. Canada, the United States, and Mexico all fish from Pacific regions anchovies in the California Modern. Climate-driven numbers and geographic dynamics may pollute the waters of collaborative aquaculture and lead to overloading. This research expands upon prior works using a game theoretic model of Tran's boundary sardine fisheries in different climatic conditions to account for ecological links. More significant economic advantages accrue from cooperation fishing tactics that consider the mackerel's role as feed for other species in the natural system, as opposed to plans that merely take note of the worth of mackerel harvests to a particular fishing nation. The maximum environmental benefit is obtained at a fishery rate for sardines barely less than the sardine Fishery Management Safe Yield. Ecological-based control of fisheries can increase sustainability and profits, but only if investors and policy makers consider the ecology in business-applicable models. Understanding and adapting to the fast alterations in habitat distributions due to climate change and designing ways to achieve viable and lucrative fishery amidst altering environments will necessitate an increased emphasis on ecosystem-based governance.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.038 | 0.003 |
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".