Expectations and realities of GPS animal location collars: results of three years in the field
Bibliographic record
Abstract
GPS collars have the potential to automatically collect large numbers of relatively accurate animal relocations. Collar costs, levels of accuracy, and satellite signal reception have been reported by other studies, but there has been little discussion of long‐term performance under field conditions. Between March 1996 and April 1999, we placed 11 GPS collars on 23 individual woodland caribou Rangifer tarandus caribou for a total of 26 collar deployments. Reliability was highly variable; some collar deployments operated normally for their expected period of time, other deployments functioned for less than half of their expected lives. Collars attempted 41,822 locations and collected 15,247 3‐D and 10,411 2‐D locations, for an average acquisition rate of 59%. We recommend that researchers carefully consider project objectives, budget constraints, and available options such as differential correction and remote collar communication, before purchasing GPS collars.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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 teacher head, 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".