Archive eggs: A research and management tool for avian conservation breeding
Bibliographic record
Abstract
Abstract Worldwide, approximately 168 bird species are captive‐bred for reintroduction into the wild. Programs tend to be initiated for species with a high level of endangerment. Depressed hatching success can be a problem for such programs and has been linked to artificial incubation. The need for artificial incubation is driven by the practice of multiclutching to increase egg production or by uncertainty over the incubation abilities of captive birds. There has been little attempt to determine how artificial incubation differs from bird‐contact incubation. We describe a novel archive (data‐logger) egg and use it to compare temperature, humidity, and egg‐turning in 5 whooping crane ( Grus americana ) nests, 4 sandhill crane ( G. canadensis ) nests, and 3 models of artificial incubator; each of which are used to incubate eggs in whooping crane captive‐breeding programs. Mean incubation temperature was 31.7° C for whooping cranes and 32.83° C for sandhill cranes. This is well below that of the artificial incubators (which were set based on a protocol of 37.6° C). Humidity in crane nests varied considerably, but median humidity in all 3 artificial incubators was substantially different from that in the crane nests. Two artificial incubators failed to turn the eggs in a way that mimicked crane egg‐turning. Archive eggs are an effective tool for guiding the management of avian conservation breeding programs, and can be custom‐made for other species. They also have potential to be applied to research on wild populations. © 2012 The Wildlife Society.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".