Survival of surf scoters and white‐winged scoters during remigial molt
Bibliographic record
Abstract
ABSTRACT Quantifying sources and timing of variation in demographic rates is necessary to determine where and when constraints may exist within the annual cycle of organisms. Surf scoters (Melanitta perspicillata) and white‐winged scoters (M. fusca) undergo simultaneous remigial molt during which they are flightless for >1 month. Molt could result in reduced survival due to increased predation risk or increased energetic demands associated with regrowing flight feathers. Waterfowl survival during remigial molt varies across species, and has rarely been assessed for sea ducks. To quantify survival during remigial molt, we deployed very high frequency (VHF) transmitters on surf scoters (n = 108) and white‐winged scoters (n = 57) in southeast Alaska and the Salish Sea (British Columbia and Washington) in 2008 and 2009. After censoring mortalities potentially related to capture and handling effects, we detected no mortalities during remigial molt; thus, estimates of daily and period survival for both scoter species during molt were 1.00. We performed sensitivity analyses in which mortalities were added to the dataset to simulate potential mortality rates for the population and then estimated the probability of obtaining a dataset with 0 mortalities. We found that only at high survival rates was there a high probability of observing 0 mortalities. We conclude that remigial molt is normally a period of low mortality in the annual cycle of scoters. The molt period does not appear to be a constraint on scoter populations; therefore, other annual cycle stages should be targeted by research and management efforts to change population trajectories. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.
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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 | 0.001 |
| 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 | 0.000 |
| 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".