A First Count of Thick-billed Murres (<i>Uria lomvia</i>) and Black-legged Kittiwakes (<i>Rissa tridactyla</i>) Breeding on Bylot Island
Bibliographic record
Abstract
Bylot Island, part of Sirmilik National Park, supports two major breeding colonies of intermingled Thick-billed Murres (Uria lomvia) and Black-legged Kittiwakes (Rissa tridactyla): at Cape Hay near the northwest tip and at Cape Graham Moore at the opposite end of the island. Although the size of these colonies has been estimated previously, there is no information on how the estimates were made, except for Thick-billed Murres at Cape Hay in 1977, when the numbers were based on sampling only about 30% of the colony. In 2013, high-resolution digital photographs of the whole area of both colonies were taken in July, when most birds were probably incubating eggs. Individual birds were counted on the photographs, and the numbers were corrected for image quality and converted to numbers of breeding pairs based on correction factors from another High Arctic colony. Our estimates were similar to those from earlier years for kittiwakes at Cape Graham Moore and for murres at Cape Hay, but suggested that numbers of murres were higher than previously thought at Cape Graham Moore, while numbers of kittiwakes were lower at Cape Hay. The overall total for the Canadian population of Thick-billed Murres was unaffected by these changes, but the total number of Black-legged Kittiwakes nesting in the Canadian Arctic may be 20% lower than previously thought.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".