Estimating geolocator accuracy for a migratory songbird using live ground-truthing in tropical forest
Bibliographic record
Abstract
Miniaturized light-level geolocators allow year-round tracking of small migratory birds, but most studies use calibration only at breeding sites to estimate geographic positions. Ground-truthing of positions in tropical habitat is needed to determine how accurate breeding site calibrations (i.e. sun elevations) are for estimating location of winter sites. We tested the accuracy of geographic assignments using geolocator data collected from Wood Thrushes ( Hylocichla mustelina ) in Central America. For a given light threshold, sun elevation angle was higher in the tropics than at breeding sites and also varied significantly at tropical winter sites between wet (Oct- Dec) and dry (Jan-Mar) seasons. However, estimation of Wood Thrush territory latitude did not differ significantly when using breeding or tropical dry season sun elevation. Average error in assignment to tropical sites was 365 ± 97 km (0.2-4.4°) in latitude. To obtain the best latitude estimates in the tropics with geolocators, we recommend using locations during the dry season where sun elevations are closer to those measured at breeding sites. We emphasize the importance of longitude in assigning forest birds to unknown sites; longitude estimates for Wood Thrushes in the tropics were, on average, within 66 ± 13 km (0-0.6°) of actual longitude. Latitude estimates were more accurate (180 ± 48 km) when assigning birds to breeding sites using deployments of geolocators in the tropics. Studies of species that are territorial in winter could collect more accurate migratory connectivity data by deploying geolocators at tropical wintering sites.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| 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 | 0.001 |
| 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 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".