Trend of White-tailed Eagles Breeding in Japan During the Past Quarter-century
Bibliographic record
Abstract
The number of breeding pairs of White-tailed Eagle (Haliaeetus albicilla) in Japan has increased yearly, from only ca. 30 pairs confirmed in the early 1990’s to more than 250 pairs in 2015. Accordingly, the breeding area has also expanded from only Hokkaido, the northrnmost island of Japan, into the northen part of the main island. On the other hand, breeding success has declined from more than 80% in 1990’s to less than 60% in 2010’s. Additionally, nest tree locations of recent years have increasingly been closer to the road and human residential areas comaperd to before. This phenomenon might have occurred because of increasing population, shortage of large trees at suitable conditions for nesting, and habituation of the eagle to human activities. Meanwhile, many White-tailed Eagles and Steller’s Sea Eagles (H. pelagicus) currently distributed in Japan during the wintering period, probably after mid-1980’s, mainly eat aboundant food derived from human activities, such as fish discarded by fisheries. This situation might contribute to increase in the population of eagles with improving winter survival rate and productivity, however, also could have negative effects on the population of eagles, such as a increase in traffic accidents near the feeding sites with human activities, and on the balance of biological community. Thus, the restoration of suitable habitats with large trees for nesting and with natural food resources in winter is the most important component of any plan for White-tailed Eagle conservation in Japan.
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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 | 0.001 | 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".