Optimizing Repeat-Visit, Call-Broadcast Nocturnal Surveys for Yellow Rails (<i>Coturnicops noveboracensis</i>)
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
Due to its secretive nature and nocturnal vocalization, multi-species bird monitoring programs are not effective in surveying populations of Yellow Rails (Coturnicops noveboracensis) and, thus, species-specific survey methods should be used. To determine how to optimize nocturnal call-playback surveys of Yellow Rails, we evaluated the effects of survey methods (naïve-estimated vs. detectability-adjusted estimated occupancy, observer, number of surveys, and the use of playbacks) and temporal and environmental conditions (e.g., time, date, temperature, moon phase, seasonality, and cloud cover) on detection probability. In 2010 and 2011, 334 call-broadcast night surveys for Yellow Rail were conducted at 167 survey points within 80 wetlands in south-central Manitoba, Canada. Yellow Rail detection probability was estimated at 0.63 in both years. In 2010, the detectability-adjusted wetland occupancy rate was estimated at 0.63, and in 2011 it was estimated at 0.36. Call-broadcast surveys contributed relatively little to improving Yellow Rail detectability, but repeat surveys at each site increased the number of individuals detected. Detection probability was not correlated with the temporal or environmental variables we studied, or by observer. Surveys where call-broadcasts are not feasible, such as volunteer surveys, are still likely to result in good estimates of Yellow Rail abundances, if surveys are repeated within breeding seasons.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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