Comparing winter distribution and harvest rates of transmitter‐marked and banded‐only mallards in western Tennessee
Bibliographic record
Abstract
Abstract The miniaturization of Global Positioning Systems (GPS) transmitters is providing insights into the ecology and management of migratory bird species at biologically‐relevant spatial scales. However, transmitters and their attachment methods could bias inferred behaviors, demographic rates, and resulting management decisions. We evaluated the effects of external harness‐style GPS transmitters by comparing direct harvest rates, winter dispersal distances, and subsequent harvest distributions of mallards ( Anas platyrhynchos ) equipped with GPS transmitters ( n = 443) to a tarsal banded‐only control group ( n = 1,123) captured in western Tennessee during winters 2019–2022. We found that transmitter‐marked mallards had similar harvest rates, winter dispersal distances, and harvest distributions as banded‐only mallards. Time between capture and harvest predicted dispersal distances but there was no effect of marker type. Specifically, the average time from capture to harvest was 30 (SE = 2) and 31 (SE = 3) days for banded‐only and transmitter‐marked mallards, respectively. Harvest rate () was only 2.2% greater for transmitter‐marked mallards compared to banded‐only mallards overall, but GPS transmitters affected harvest susceptibility of juveniles ( = 14.5%). Based on harvest rates and dispersal distances between transmitter‐marked and banded‐only cohorts, we suggest 7‐ to 10‐day data censoring periods may be warranted, especially for juveniles, as mallards acclimate to GPS transmitters. Overall, we concluded that modern harness‐style GPS transmitters provided reliable information of wintering mallard space use, movements, and harvest mortality and can be used to inform ecology and management of wintering mallards and other dabbling ducks. Future studies should evaluate effects of harness‐style GPS transmitters for other species and during different portions of dabbling ducks' life cycle, such as migration or nesting, when they may experience greater adverse effects.
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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".