A novel use of passive integrated transponder (PIT) tags as nest markers
Bibliographic record
Abstract
ABSTRACT Double-observer methodology requires independent collection of data to accurately estimate population parameters. Use of visual nest markers to facilitate matching, relocating, and monitoring nests as part of a double-observer study violates this assumption, but few reliable alternatives exist, especially when working with cryptic nests and high nest densities in homogeneous habitat. We used passive integrated transponder (PIT) tags to nonvisually mark the nests of ground-nesting birds at the Yukon Delta National Wildlife Refuge in western Alaska in a double-observer study of nest density. We marked 70 nests with PIT tags and naïve observers subsequently detected tags at 44 of 50 re-scanned nests (88% correct identification). Failed detections were likely due to either suboptimal tag orientation or tags falling through nest material, and such failures may be an inherent, but uncommon, feature of this nest-marking technique. PIT tags facilitated nest monitoring among independent observers, uniquely and reliably marked nests, provided a minimum of cues to potential nest predators, and allowed us to estimate densities in a double-observer framework while not violating assumptions. These tags should be useful in other studies of nesting birds where nonvisual, reliable nest markers are needed, and they provide a new tool for double-observer studies.
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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 | 0.001 | 0.002 |
| 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".