What are farmers' perceptions about farmland landbirds? A Galapagos Islands perspective
Bibliographic record
Abstract
Abstract Conservation practices in agricultural landscapes can greatly mitigate biodiversity loss. However, agricultural landscapes are embedded in complex, social-ecological systems and therefore require a strong social-ecological approach for effective conservation measures. The Galapagos Islands are globally recognized for their high levels of biodiversity. Nevertheless, in recent years, Galapagos landbirds have suffered rapid declines, specifically in the agricultural zone. Our study is the first to examine the farmers’ perception of landbirds in the agricultural zone of Santa Cruz, Galapagos Islands. We conducted semi-structured interviews with 38 farmers to characterize the relationship between farmers and landbirds including how landbirds affect farmers and farmers’ perceptions of landbirds. The interviewed farmers managed a diverse array of farm types including coffee in agroforestry settings (23.7%), small-scale fruit and vegetable (60.5%) and livestock production (15.8%). We found that 86.9% of farmers had a positive or neutral perception of birds despite 52.6% of farmers finding finches bothersome. The most common techniques farmers employed to deter birds were putting out food and water, using nets to protect seedbeds and crops and using protective tubes around young plants. Our results suggest a positive potential for future conservation work targeted on farmland biodiversity. Future conservation projects should also address disservices and the mitigation of crop raiding by landbirds, the uninformed use of pesticides and other pest issues such as ants and rats.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".