First you get the money, then you get the power: Comparing the cost and power of monitoring programs to detect changes in occupancy of a threatened marsupial predator
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 Ecological monitoring is crucial for tracking changes in the status of species over time. However, ensuring that monitoring programs possess adequate statistical power—capacity to detect changes in populations with a high level of confidence—remains a challenge for many wildlife managers globally. While new monitoring technologies potentially offer cost effective solutions to this problem, transitioning to these methods requires careful calibration with existing techniques, such that differences in power and cost can be measured and assessed accurately. Here, we compare new (camera traps) and conventional (live trapping) survey methods in terms of cost and statistical power in tracking occupancy declines in an endangered marsupial predator, the northern quoll ( Dasyurus hallucatus ). We show that camera trap monitoring designs can detect northern quoll occupancy declines of 30%, 50%, and 80% at reduced cost when compared to live trap designs, without compromising statistical power. Overall, we find support for the cost‐effectiveness of camera traps for species monitoring and its potential to replace existing live trap sampling of species when measuring changes in occupancy. Additionally, we offer a robust framework to compare new monitoring techniques against pre‐existing methods on the basis of statistical power.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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