Peruvian anchoveta as a telecoupled fisheries system
Bibliographic record
Abstract
Fisheries are coupled human and natural systems (CHANS) across distant places, yet fisheries research has generally focused on better understanding either fisheries ecology or human dimensions in a specific place, rather than their interactions over distances.As economic and ideational globalization accelerate, fisheries are becoming more globally connected via movements of fish products and fisheries finances, information, and stakeholders throughout the world.As such, there is a pressing need for systematic approaches to assess these linkages among global fisheries, their effects on ecosystems and food security, and their implications for fisheries science and sustainability.Use of the telecoupling framework is a novel and insightful method to systematically evaluate socioeconomic and environmental interactions among CHANS.We apply the telecoupling framework to the Peruvian anchoveta (Engraulis ringens) fishery, the world's largest single-species commercial fishery and a complex CHANS.The anchoveta fishery has diverse and significant telecouplings, socioeconomic and environmental interactions over distances, with the rest of the world, including fishmeal and fish oil trade, monetary flow, knowledge transfer, and movement of people.The use of the telecoupling framework reveals complex fishery dynamics such as feedbacks (e.g., profit maximization causing fishery overcapitalization) and surprises (e.g., stock collapse) resulting from local and long-distance ecological and socioeconomic interactions.The Peruvian anchoveta fishery illustrates how the telecoupling framework can be used to systematically assess the magnitude and diversity of local and distant fisheries interactions and thereby advance knowledge derived from traditional monothematic research approaches.Insights from the telecoupling framework provide a foundation from which to develop sustainable fisheries policy and management strategies across local, national, and international levels in a globalized world.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".