EGG-TURNING BEHAVIOR AND NEST ATTENTIVENESS OF THE ENDANGERED HAWAIIAN GOOSE ON KAUA'I
Bibliographic record
Abstract
We used infrared video cameras to obtain the first quantitative measurements of the frequency and details of egg-turning behavior in wild Hawaiian Geese or Nene (Branta sandvicensis). We recorded a total of 240 hr of video, of which 53.8% was at night, over 7 days at two nests in Kilauea Point National Wildlife Refuge, Kauai, Hawaii. The mean of the two females’ daytime egg-turning frequency (1.15 turns/hr) was similar to the value reported for other waterfowl and identical with that of the Canada Goose (Branta canadensis), from which the Nene is thought to be derived. One female turned her eggs less frequently at night, a pattern typical of waterfowl, whereas the other did not. The total number of bouts of egg turning per 24 hr averaged 25.7 for one female and 22.7 for the other. Waterfowl almost always turn their eggs by rotating them solely about the long axis, yet one of these Nene rotated one egg (occasionally two) 180° about the short axis during 21% of egg-turning bouts. We observed a combined total of 65 incubation recesses by the two females, of which 16.8% occurred at night. One of the birds took longer recesses and spent substantially more time away from the nest than the other. As on other Hawaiian islands, the Nene on Kauai tended to take recesses more frequently near dawn and dusk. The two females’ nest attentiveness differed during the daytime but not when averaged over 24 hr.
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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 | 0.000 | 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) | 0.001 | 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".