Trade‐offs in performance of six lightweight automated tracking devices for birds
Bibliographic record
Abstract
Researchers should consider the costs and benefits of using tracking devices and choose devices that will optimize information gained with minimal effects on study organisms. With numerous technological advancements and devices marketed for avian research, selecting an optimal device and data collection interval (i.e., duty cycle) can be difficult. We evaluated six tracking-device types from two manufacturers (Pinpoint 10 [1-g; Lotek], Pinpoint Argos 75 [4-g; Lotek], Pinpoint Argos Solar S [6-g; Lotek], Ornitrack-10 [10-g; Ornitela], Ornitrack-15 [15-g; Ornitela], and Ornitrack-N35 [35-g; Ornitela]) and varied duty cycles to quantify (1) fix success rate for all units, (2) precision of location information for all units, and (3) battery voltage given the effects of duty cycle and reduced light for solar-rechargeable units (Ornitrack-10, Ornitrack-15, and Ornitrack-N35). Fix success rates for Pinpoint 10, Pinpoint Argos 75, Ornitrack-10, Ornitrack-15, and Ornitrack-N35 units were overall > 0.95. However, the Pinpoint Argos Solar S units had a lower fix success rate that varied with duty cycle intensity. The Pinpoint Argos 75, Pinpoint Argos Solar S, and Ornitrack-10 units were more precise (≥ 99% points were collected within 20 m of each other) than the Pinpoint 10, Ornitrack-15, and Ornitrack-N35 units (> 80% of points collected within 20 m of each other). For all devices, batteries maintained a high charge (i.e., battery charge lost during the dark hours was recovered or mostly recovered during the day) under high and intermediate light levels, and low and intermediate duty cycles. Light explained more variation in battery voltage than duty cycle. We encourage investigators to evaluate devices prior to deployment on birds to maximize data quality relative to their research questions.
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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) | 0.002 | 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".