Regional and annual variability in common eider nesting ecology in Labrador
Bibliographic record
Abstract
Nesting densities are often used to estimate breeding population size and with other measures of reproductive performance can be useful indicators of population status. These aspects of breeding biology often show considerable spatial and temporal variation. Between 2000 and 2003, we \nsurveyed nesting common eiders (Somateria mollissima) on 172 islands in three archipelagos (Nain, Hopedale, Rigolet) on the Labrador coast. Rigolet was the largest archipelago (2834 km2) followed by Nain then Hopedale, and island density varied inversely with archipelago size. Overall \nmeans were: nest density 52.0 ± 141.9 (SD) nests/ha; nest initiation 12 June ± 12 days; clutch size 3.7 ± 1.2 eggs/nest; egg volume 98.8 ± 10.4 cm3; and clutch volume 392.3 ± 135.0 cm3. Rigolet had the highest average egg \nvolumes and nest densities, the highest single island nest density of 1053 nests/ha, and the earliest average nest initiation date. We found significant differences in nest densities among archipelagos and across years; \nsignificant archipelago and year interactions were detected for nest initiation date and clutch size. Signifi cant differences were found among individual islands for all response variables except egg volume. For egg \nvolume, within-archipelago island differences were not signifi cant, but between-archipelago differences were signifi cant. Thus egg volume may be a useful diagnostic to identify population affi liation.
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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 | 0.001 |
| 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".