Distribution of common eiders Somateria mollissima during the brood‐rearing and moulting periods in the St. Lawrence Estuary, Canada
Bibliographic record
Abstract
Abstract Common eiders Somateria mollissima nest in colonies on islands of the St. Lawrence Estuary in Quebec, Canada. After hatching, attending females must reach suitable brood‐rearing habitats while non‐attending females and adult males must find appropriate moulting sites. The aim of our study was to determine the biotic and abiotic factors that influence the distribution of common eiders during the brood‐rearing and moulting periods. We conducted biweekly surveys and recorded the number of ducks by age and sex in 2003 and 2004 at 68 sites along a 200‐km stretch of the south shore of the St. Lawrence River. We further classified adult females according to the presence or absence of young. We evaluated human disturbance during the surveys and developed a sinuosity index describing shoreline protection. We determined the nature of the substrate and an estimate of food abundance for a subsample of sites (N = 38). At the scale of the estuary, common eiders did not distribute randomly but used the same sites in both years. Broods preferred mainland sites near nesting islands. Non‐maternal females and males were located further east along the estuary, but their numbers were lower than expected based on the size of the breeding population, indicating that some birds undertake a moult migration outside of the area. In the eastern portion of our study area, densities of non‐maternal females increased significantly as summer progressed. The distribution of common eiders was influenced by food abundance and type but was not related to our indices of human disturbance and shoreline protection. Males were associated with mussels, non‐maternal females with both mussels and gammarids, while maternal females with ducklings were associated with periwinkles. Few of the sites used by common eiders along the south shore of the St. Lawrence Estuary are currently protected and measures should thus be taken to insure their conservation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".