Great Horned Owls Affect Herring Gull Nest Attentiveness
Bibliographic record
Abstract
Herring Gull (Larus argentatus) populations in Pukaskwa National Park have declined by 70% over the last 40 years. Populations of avian predators that prey on Herring Gulls have increased which could be a significant factor impacting gull populations. Here, we investigate Herring Gull daytime and nighttime nest attentiveness at locations with and without evidence of nocturnal predators. In 2017, Herring Gull nest attentiveness was examined at two sites using remote cameras. At one of those sites Great Horned Owl (Bubo virginianus) predation was observed, and gull nighttime nest attentiveness was lower there than at the site where owls were not observed. There were no inter-site differences in daytime nest attentiveness. In 2018, Herring Gull nest attentiveness was further investigated at the site where owls were present. At that site, Herring Gull nighttime nest attentiveness was significantly lower than during the day. Extended periods of absence of gulls from their nests during the night corresponded with the presence of owls. Predation of nest contents, in addition to the effects of other environmental stressors, are likely contributing to declines in Pukaskwa National Park's Herring Gull population.
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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 | 0.000 |
| 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) | 0.001 | 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".