Double‐tagging scores of seabirds reveals that light‐level geolocator accuracy is limited by species idiosyncrasies and equatorial solar profiles
Bibliographic record
Abstract
Abstract Light‐level geolocators are popular bio‐logging tools, with advantageous sizes, longevity and affordability. Biologists tracking seabirds often presume geolocator spatial accuracies between 186 and 202 km from previously innovative, yet taxonomically, spatially and computationally limited, studies. Using recently developed methods, we investigated whether assumed uncertainty norms held across a larger‐scale, multispecies study. We field‐tested geolocator spatial accuracy by synchronously deploying these with GPS loggers on scores of seabirds across five species and 11 Mediterranean Sea, east Atlantic and south Pacific breeding colonies. We first interpolated geolocations using the geolocation package FLightR without prior knowledge of GPS tracked routes. We likewise applied another package, probGLS , additionally testing whether sea‐surface temperatures could improve route accuracy. Geolocator spatial accuracy was lower than the ~200 km often assumed. probGL S produced the best accuracy (mean ± SD = 304 ± 413 km, n = 185 deployments) with 84.5% of GPS‐derived latitudes and 88.8% of longitudes falling within resulting uncertainty estimates. FLightR produced lower spatial accuracy (408 ± 473 km, n = 171 deployments) with 38.6% of GPS‐derived latitudes and 23.7% of longitudes within package‐specific uncertainty estimates. Expected inter‐twilight period (from GPS position and date) was the strongest predictor of accuracy, with increasingly equatorial solar profiles (i.e. closer temporally to equinoxes and/or spatially to the Equator) inducing more error. Individuals, species and geolocator model also significantly affected accuracy, while the impact of distance travelled between successive twilights depended on the geolocation package. Geolocation accuracy is not uniform among seabird species and can be considerably lower than assumed. Individual idiosyncrasies and spatiotemporal dynamics (i.e. shallower inter‐twilight shifts by date and latitude) mean that practitioners should exercise greater caution in interpreting geolocator data and avoid universal uncertainty estimates. We provide a function capable of estimating relative accuracy of positions based on geolocator‐observed inter‐twilight period.
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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 | 0.002 |
| 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 | 0.001 | 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".