Kleptoparasitism of a Coyote (Canis latrans) by a Golden Eagle (Aquila chrysaetos) in Northwestern Canada
Bibliographic record
Abstract
calves in the diet of nesting Golden Eagles in Finnmark, northern Norway. Ornis Fennica 84: 112-118. Kroll AM. 1993. Haul out patterns and behavior of harbor seals, Phoca vitulina, during the breeding season at Protection Island, Washington [thesis]. Seattle, WA: University of Washington, 142 p. norberg H, kojola I, AlKIO P, nylund M. 2006. Predation by Golden Eagle Aquila chrysaetos on semi-domesticated reindeer Rangifer tarandus calves in northeastern Finnish Lapland. Wildlife Biology 12:393-402. Parrish JK, Marvier M, Paine RT. 2001. Direct and indirect effects: Interactions between bald eagles and common murres. Ecological Applications 11: 1858-1869. Sherrod SK, Estes JA, White CM. 1975. Depreda tion of sea otter pups by bald eagles at Amchitka Island, Alaska. Journal of Mammalogy 56:701 703. Sherrod SK, White CM, Williamson FSL. 1976. Bi ology of the bald eagle on Amchitka Island, Alas ka. The Living Bird 15:143-182. skorupa JP. 1989. Crowned Eagles Strephanoaetus co ronatus in rainforest: Observations on breeding chronology and diet at a nest in Uganda. Ibis 131: 294-298. stalmaster M. 1987. The bald eagle. New York, NY: Universe Books. 227 p. thompson SP. 1989. Observations of bald eagles eat ing glaucous-winged gull eggs in western Wash ington. Northwestern Naturalist 70:13-14. Tjernberg M. 1981. Diet of the Golden Eagle Aquila chrysaetos during the breeding season in Sweden. Ecography 4:12-19. Watson JW, Stinson D, McAllister KR, Owens TE. 2002. Population status of bald eagles breeding in Washington at the end of the 20th century. Jour nal of Raptor Research 36:161-169.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".