Analyzing geolocator data for birds that roost in cavities year-round
Bibliographic record
Abstract
The use of light-level geolocators for monitoring migration has been limited to non-cavity roosting species because light transitions for cavity-roosting species are obscured. Using Northern Flickers (Colaptes auratus), nocturnal cavity-roosting woodpeckers, as a model, I describe a method for analyzing geolocator data that initially adjusts light transitions to account for differences between the time of minimum light threshold and when a bird enters or exits a cavity. Using known locations from the breeding grounds, I assessed the precision of this adjustment method for estimating location by examining the associated error, the repeatability of the length of time individuals roosted in cavities, and by conducting a sensitivity analysis to assess uncertainty. Mean location error decreased from 1417 ± 277 km (SD) to 129 ± 194 km when sunrise and sunset times were adjusted and locations from >25 d were averaged. Sensitivity analysis showed that if an adjusted sunrise or sunset time was “incorrect” by 10 min, the error was 121–137 km from the actual location. This adjustment method significantly improved location estimates at known sites, suggesting that adjusting light transitions based off a calibration is a good initial step for determining location. However, to account for behavioral changes in entrance and emergence times, applying state-space Kalman filter models can further improve the accuracy of location estimates. The combination of adjusting transitions and applying a state-space Kalman filter thus allows location estimates to be obtained from cavity-roosting species using geolocator data.
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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 | 0.003 |
| 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 | 0.000 |
| 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 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".