THE PRELAYING INTERVAL OF EMPEROR GEESE ON THE YUKON-KUSKOKWIM DELTA, ALASKA
Bibliographic record
Abstract
We marked 136 female Emperor Geese(Chencanagica) in western Alaskawith VHF or satellite (PTT) transmittersfrom 1999 to 2003 to monitor their spring arrivaland nest initiation dates on the Yukon Delta, andto estimate prelaying interval lengths once at thenesting area. Ninety-two females with functionaltransmitters returned to the Yukon Delta in thespring after they were marked, and we located thenests of 35 of these individuals. Prelayingintervals were influenced by when snow melted inthe spring and individual arrival dates on theYukon Delta. The median prelaying interval was15 days (range = 12–19 days) in a year when snowmelted relatively late, and 11 days(range = 4–16 days) in two warmer yearswhen snow melted earlier. In years when snow meltedearlier, prelaying intervals of <12 daysfor 11 of 15 females suggested they initiated rapidfollicle development on spring staging areas. Theprelaying interval declined by approximately0.4 days and nest initiation date increasedapproximately 0.5 days for each day a femaledelayed her arrival. Thus, females that arrivedfirst on the Yukon Delta had prelaying intervals upto four days longer, yet they nested up to fivedays earlier, than females that arrived last. Theproximity of spring staging areas on the AlaskaPeninsula to nesting areas on the Yukon Delta mayenable Emperor Geese to alter timing of follicledevelopment depending on annual conditions, and toinvest nutrients acquired from both areas in eggsduring their formation. Plasticity in timing offollicle development is likely advantageous in avariable environment where melting of snow cover inthe spring can vary by 2–3 weeksannually.
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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".