Changes in nesting-habitat use of large gulls breeding in Witless Bay, Newfoundland
Bibliographic record
Abstract
We counted herring gull (Larus argentatus) and great black-backed gull (Larus marinus) nests in the Witless Bay Seabird Ecological Reserve in southeastern Newfoundland, Canada, in 1999 and 2000 and compared our results with previous nest counts from the 1970s. On Gull Island, herring gull nest numbers were 27.5% (1999) and 30.0% (2000) lower than in 1979. Similarly, on Great Island, by 2000 the numbers of herring gull nests had declined 40.8% from numbers in 1979. Counts of great black-backed gull nests were more variable, but suggest a slight or no reduction since 1979. Numbers of herring gulls nesting in rocky and puffin-slope habitats were much reduced (5070%), while numbers nesting in meadows and forests have actually increased since the 1970s. Great black-backed gulls showed a similar change in nesting distribution. For herring gulls, these changes in nesting numbers matched differences in reproductive success previously documented in these habitats. We suggest that the decline in gull numbers and the change in breeding-habitat selection were caused by changes in the food availability for gulls. Reduced amounts of fisheries offal and the delayed arrival onshore of capelin (Mallotus villosus), an important fish prey species for gulls, have all likely led to the decline in gull reproductive output. Gulls nesting in meadows and forests may be maintaining adequate reproductive output by focusing on alternative prey, such as adult Leach's storm-petrels (Oceanodroma leucorhoa), rather than scarce refuse and fish.
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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".