Ecosystem approach to inland fisheries: research needs and implementation strategies
Bibliographic record
Abstract
Inland fisheries are a vital component in the livelihoods and food security of people throughout the world, as well as contributing huge recreational and economic benefits. These valuable assets are jeopardized by lack of research-based understanding of the impacts of fisheries on inland ecosystems, and similarly the impact of human activities associated with inland waters on fisheries and aquatic biodiversity. To explore this topic, an international workshop was organized in order to examine strategies to incorporate fisheries into ecosystem approaches for management of inland waters. To achieve this goal, a new research agenda is needed that focuses on: quantifying the ecosystem services provided by fresh waters; quantifying the economic, social and nutritional benefits of inland fisheries; improving assessments designed to evaluate fisheries exploitation potential; and examining feedbacks between fisheries, ecosystem productivity and aquatic biodiversity. Accomplishing these objectives will require merging natural and social science approaches to address coupled social-ecological system dynamics.
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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 | 0.049 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.014 | 0.006 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".