Human interference with wildlife surveys: a case study from camera-trapping road underpasses in Costa Rica
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Camera traps are widely used to study wildlife. However, theft and vandalism are frequent, resulting in millions of dollars in financial losses and large data gaps in research. Here we report on the impacts of camera-trap theft on a study examining wildlife movement under highway bridges in south-west Costa Rica. Even with metal cases, locks and signs installed on all camera traps, 65% were stolen. The working camera traps accumulated a total of 167 trap-nights and detected only two wild mammal species, eight bird species and one reptile species, as well as three domestic animal species and people. This limited number of wild species was unexpected given the known presence of wide-ranging megafauna and a diverse terrestrial mammal community in the region. The pervasive theft of camera traps leads to data gaps and impairs the potential for research in the region, and we discuss the potential additional reasons for detecting only a small number of species. Our findings highlight the need for solutions to camera-trap theft, to limit financial and data losses for conservation.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| 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 it