Wildlife and global warming Navigating the Arctic Meltdown
Bibliographic record
Abstract
ivory gulls Arctic lore is rife with the ghosts of doomed voyages and other legends, but the story of a pale seabird disappearing from its icy haunts is no tall tale. The ivory gull is, in fact, literally losing ground as rising temperatures melt its polar sea-ice habitat. Aerial surveys of ivory gull breeding colonies, bird counts conducted at sea and the observations of local native people all point to a precipitous fall in Canadian populations. A recent aerial survey of nesting ivory gulls documented an 80 percent decline in the number of breeding birds since the 1980s. Surveyors found several of the largest colonies completely extirpated and significantly fewer nesting birds in the remaining colonies. At sea, where the gulls forage and feed in the polar icepack, researchers aboard cruising icebreakers in 2002 saw less than a third of the number of ivory gulls seen in 1993, and no ivory gulls scavenging around polar bear kills on the sea ice. Canadian Inuit communities with firsthand knowledge of this seabird, which shares their isolated homeland, also note a downturn. These observations alarm conservationists. “[Surveys] showed a really significant decline in the number of birds nesting in Nunavat, which is the only place they nest in Canada, ” says Dick Canning, a member of the Committee on the Status of Endangered Wildlife in Canada. Adds Mark Mallory, a seabird biologist with the Canadian
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".