Bibliographic record
Abstract
Top predators of the arctic tundra are facing a long period of very low prey availability during winter and subsidies from other ecosystems such as the marine environment may help to support their populations. Satellite tracking of snowy owls, a top predator of the tundra, revealed that most adult females breeding in the Canadian Arctic overwinter at high latitudes in the eastern Arctic and spend several weeks (up to 101 d) on the sea-ice between December and April. Analysis of high-resolution satellite images of sea-ice indicated that owls were primarily gathering around open water patches in the ice, which are commonly used by wintering seabirds, a potential prey. Such extensive use of sea-ice by a tundra predator considered a small mammal specialist was unexpected, and suggests that marine resources subsidize snowy owl populations in winter. As sea-ice regimes in winter are expected to change over the next decades due to climate warming, this may affect the wintering strategy of this top predator and ultimately the functioning of the tundra ecosystem. The Arctic tundra is characterized by strong seasonal variations in productivity, which nonetheless remains relatively low throughout the year compared to other ecosystems (Bliss 1986, Gauthier et al. 1996, Krebs et al. 2003). Considering that, the relatively high abundance and diversity of vertebrate predators in the tundra is impressive
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".