Community‐based monitoring, assessment and management of data‐limited inland fish stocks in North Rupununi, Guyana
Bibliographic record
Abstract
Abstract Inland fisheries are important for food security in communities around the world, especially in developing countries. In North Rupununi, Guyana, the state of exploited stocks is poorly understood, and fishery monitoring and assessment are challenging because diverse fishing gears and target species are distributed across a heterogeneous landscape. This complexity created an opportunity for community‐based monitoring (CBM) to support data‐limited assessment. Standardised CBM was established for the North Rupununi as part of a new inland fisheries management plan initiated by indigenous community groups with support from the government. Quantitative length‐based assessments undertaken for target stocks suggested moderate levels of exploitation consistent with local perception. Our study highlights that local experts and community participants with different levels of training can collect accurate biodiversity data. Further development of CBM is important in North Rupununi. We recommend using local ecological knowledge indicators to track spatial and temporal patterns in exploitation and fish stock status.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".