Drive-By Netting: A Technique for Capturing Grebes and Other Diving Waterfowl
Bibliographic record
Abstract
We describe a new method (drive-by netting) for capturing grebes (Podiceps spp.) and other birds that dive under water to escape capture. We used a floating gill net to capture 203 eared grebes (Podiceps nigricollis) in 20 days in 1999 on the Great Salt Lake (GSL), 652 eared grebes in 41 days on the GSL in 2000, and 409 grebes in 20 days in 2001. Other species captured during the 2000 and 2001 fi eld seasons included 1 western grebe (Aechmophorus occidentalis), 9 ruddy ducks (Oxyura jamaicensis), and 1 Canada goose (Branta canadensis). Two people, a motorboat, and a gill net are required for drive-by netting. Our method was efficient, having a high capture rate per unit effort and a low mortality rate. Drive-by netting can be used to capture both individual grebes and large numbers of grebes on open water.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".