Accuracy, Precision, and Observation Rates of Global Positioning System Telemetry Collars
Bibliographic record
Abstract
ABSTRACT We addressed concerns regarding performance of various Global Positioning System (GPS) collar configurations for describing habitat use by Rocky Mountain elk ( Cervus elaphus ) in rugged, forested terrain. We tested 8 GPS collars (Lotek Wireless, Newmarket, ON, Canada) in 4 different model and equipment configurations at 2 reference points (an open hilltop and a forested ravine) to determine habitat‐specific differences in performance among collar configurations. We then placed individual collars at 60 additional points that were stratified randomly among 4 canopy‐cover classes and 3 classes of available sky. All collars exhibited a locational bias of 4 m horizontally west and of 10 m vertically below a reference standard established by position‐averaging with a handheld receiver (Garmin 12MAP) calibrated at National Geodetic Survey benchmarks. The GPS collar models that were programmed for longer satellite‐acquisition times provided greater location precision than models that had been programmed for short acquisition times to preserve battery power. Canopy cover and available sky had a greater effect on collar location precision and observation rates than slope, slope position, aspect, conifer basal area, tree height, canopy depth, or elevation. Researchers should test collars at known reference points to confirm that location precision and rates of observation are adequate for their particular study objectives. Manufacturers of GPS collars should inform clients of their programming criteria for acquisition time so that customers can make informed decisions regarding trade‐offs between precision of locations, data quantity, and battery life.
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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 | 0.008 | 0.031 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".