A national assessment of waterbird hunting in coastal wetlands of Suriname, South America
Bibliographic record
Abstract
Abstract The central northern coast of South America has extensive wetlands critical for waterbird conservation. While waterbird harvest occurs in the region, the impact on species population dynamics remains unclear. This study assesses waterbird hunting in the coastal wetlands of Suriname, addressing: (i) the extent of waterbird harvest, (ii) changes in harvest magnitude over time, and (iii) the motivations and methods used by hunters. We collected data via a national survey of licensed hunters in 2006 and 2016 using structured interviews. A Bayesian hierarchical model was used to analyze the data. We estimated harvest levels for 11 species and three groups (small herons, small shorebirds, large shorebirds). For most species, mean harvest per hunter significantly decreased from 2006 to 2016, except for blue-winged teal and migratory shorebirds. Most hunting was for non-commercial purposes (personal consumption and recreational). This is the first national assessment of waterbird hunting in Suriname. Harvest levels vary by species, and the sustainability of these levels remains uncertain. Managing hunting in Suriname requires addressing both legal and illegal hunting. Given Suriname’s importance for waterbirds, particularly species like the scarlet ibis and migratory shorebirds, it should be a priority for conservation efforts.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".