Differential Declines among Nesting Habitats of Breeding Herring Gulls (<i>Larus argentatus</i>) and Great Black-Backed Gulls (<i>Larus marinus</i>) in Witless Bay, Newfoundland and Labrador, Canada
Bibliographic record
Abstract
Environmental conditions in eastern Newfoundland have changed considerably since the 1970s, as both bottom-up oceanographic and anthropogenic influences on seabird populations have fluctuated considerably. The diet, reproductive success, and presumably survival of gulls are intrinsically linked to these processes, and breeding populations have declined considerably through the 1980s and 1990s. To assess the populations of breeding large gulls in the Witless Bay Ecological Reserve in eastern Newfoundland and Labrador, Canada, nests were surveyed and clutch size determined for Herring Gulls (Larus argentatus) and Great Black-backed Gulls (L. marinus) breeding on Great, Gull, and Pee Pee Islands in 2011–2012. The total number of breeding gulls of these two species combined decreased by 41% on Gull Island, 78% on Great Island and 51% on Pee Pee Island since 2000. However, the declines differed among habitat type, with modest declines on puffin slopes (-15% to -52%) and the steepest declines in meadows (-70% to -88%), suggesting that large-scale causative factors are not solely responsible for changes in population size. Clutch size did not differ from that in 2000. Differential recruitment among highly philopatric gulls stemming from bottom-up diet-related variation in breeding success may be responsible for different changes in populations among different habitats.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".