Techniques for identifying predators of goose nests
Bibliographic record
Abstract
We used cameras and artificial eggs to identify nest predators of dusky Canada goose Branta canadensis occidentalis nests during 1997–2000. Cameras were set up at 195 occupied goose nests and 60 artificial nests. We placed wooden eggs and domestic goose eggs that were emptied and then filled with wax or foam in an additional 263 natural goose nests to identify predators from marks in the artificial eggs. All techniques had limitations, but each correctly identified predators and estimated their relative importance. Nests with cameras had higher rates of abandonment than natural nests, especially during laying. Abandonment rates were reduced by deploying artificial eggs late in laying and reducing time at nests. Predation rates for nests with cameras were slightly lower than for nests without cameras. Wax-filled artificial eggs caused mortality of embryos in natural nests, but were better for identifying predator marks at artificial nests. Use of foam-filled artificial eggs in natural nests was the most cost effective means of monitoring nest predation.
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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".