Coupling Tracking Technologies to Maximize Efficiency in Avian Research
Bibliographic record
Abstract
ABSTRACT Direct marking and tracking of wildlife using telemetry is widespread and critical to understanding many aspects of wildlife ecology. For most species, researchers must select between multiple tracking technologies that represent trade‐offs among data requirements, mass, and cost. Options tend to be more limited for smaller, volant species. We developed and tested a unique combination of a store‐on‐board Global Positioning System logger with an independent very‐high‐frequency (VHF) tag (hereafter, hybrid tag) fitted on the greater sage‐grouse ( Centrocercus urophasianus ) with a modified harness design in northeastern Wyoming, USA, 2017–2018. We compared hybrid tags with other tracking technologies commonly used in avian research, namely VHF and Argos satellite relay tags. Given our research objectives, that required both frequent location data and field‐based observational data, we found the hybrid tags were the most cost‐effective option and capable of collecting more location data compared with Argos tags because of power savings associated with data transmission. Cost savings allowed us to avoid sacrificing sample size while still obtaining high‐resolution location data in addition to field‐based observational data such as the presence of chicks. We believe our hybrid tags and harness design would be beneficial to research on other avian species of comparable size to the greater sage‐grouse and those that are relatively localized year‐round, including many other Galliformes. © 2020 The Wildlife Society.
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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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".