Accuracy and performance of low‐feature GPS collars deployed on bison <i>Bison bison</i> and caribou <i>Rangifer tarandus</i>
Bibliographic record
Abstract
Recently, a new generation of global positioning system (GPS) collars has become available that provides limited daily location fixes, a relatively long battery life, and are low‐cost, compared to full‐featured GPS collars. However, their performance is untested, and assessing these biases is needed to inform study designs and data analysis protocols. We used stationary tests of 15 Lotek LifeCycle GPS collars – an example of low‐feature GPS collars – to measure the accuracy of location fixes. In addition, we deployed Lotek Lifecycle GPS collars on 153 caribou Rangifer tarandus and 24 bison Bison bison in Yukon and Alaska, to assess their field performance. We examined differences among species, sex, location fix schedule, and latitude, on four performance metrics (FSR, 3D‐V FSR, DOP and DSR). Stationary trials indicated that mean precision (4.3 ± 4.0 m [SD]) and accuracy (6.0 ± 4.7 m) of location fixes was excellent, and FSR was good (87.2%), albeit both were slightly affected by forest canopy cover. Field performance varied by species and sex. Notably, the mean DSR for male bison was dismal (27.4 ± 24.2%) likely because of their behaviour, and the mean FSR, 3DV‐FSR and DOP, for male caribou was poor (FSR = 57.3 ± 2.0%), compared to collars deployed on female caribou (72.2 ± 1.7%) or female bison (77.9 ± 1.4%). We also observed that the VHF transmitters often failed when the collar malfunctioned. Biases in the accuracy and performance of these low‐cost GPS collars should be taken into account when designing studies. Researchers contemplating investing in low‐feature GPS collars require information on their ‘real‐world’ performance so that they can decide whether they are appropriate for their intended application. Moreover, researchers need to consider biases in their GPS collar data prior to embarking on field studies and when conducting analyses with the data collected from them.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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".