Predation rates, timing, and predator composition for Scoters (<i>Melanitta</i>spp.) in marine habitats
Bibliographic record
Abstract
Studies of declining populations of sea ducks have focused mainly on bottom–up processes with little emphasis on the role of predation. We identified 11 potential predators of White-winged Scoters ( Melanitta fusca (L., 1758)) and Surf Scoters ( Melanitta perspicillata (L., 1758)) in North American marine habitats. However, of 596 Scoters marked with VHF transmitters along the Pacific coast, mortalities were recovered in association with just two identifiable categories of predators: in southeast Alaska recoveries occurred mainly near mustelid feeding areas, while those in southern British Columbia and Washington occurred mainly near feeding areas of Bald Eagles ( Haliaeetus leucocephalus (L., 1766)). Determining whether marked Scoters had been depredated versus scavenged was often not possible, but mortalities occurred more frequently during winter than during wing molt (13.1% versus 0.7% of both species combined, excluding Scoters that died within a postrelease adjustment period). In two sites heavily used by Scoters, diurnal observations revealed no predation attempts and low rates of predator disturbances that altered Scoter behavior (≤0.22/h). These and other results suggest that predation by Bald Eagles occurs mainly at sites and times where densities of Scoters are low, while most predation by mustelids probably occurs when Scoters are energetically compromised.
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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".