Changing winter diet of Thick-billed Murres (<i>Uria lomvia</i>) in southwest Greenland, 1990s versus 2010s
Bibliographic record
Abstract
Southwest Greenland constitutes an internationally important wintering area for seabirds, including Thick-billed Murres (Uria lomvia (Linnaeus, 1758)), but their prey may be affected by the general warming of this sub-Arctic region. We compared murre diet collected in winter in the 1990s and in the 2010s around Nuuk, Greenland. Fish made up 36% of the diet (wet mass) and crustaceans 63% in the 1990s, changing to 22% and 78% in the 2010s, respectively. Capelin (Mallotus villosus (Müller, 1776)) was the dominant fish species, and the smaller contribution in the 2010s coincided with declining densities of capelin around Nuuk. The crustaceans were dominated by two krill species (Meganyctiphanes norvegica (M. Sars, 1857) and Thysanoessa inermis (Krøyer, 1846)). However, M. norvegica was only important in the 2010s (51% wet mass), while T. inermis was dominating the 1990s with 62% wet mass and only 23% in the 2010s. The dominance of M. norvegica in the 2010s confirmed our expectations of a gradual “borealization” of this region due to the generally warming sub-Arctic. The smaller contribution of fish in the diet may also support the hypothesis of deteriorating winter conditions for murres. Apart from the diet, plastic was found in 15% of the birds and 53% had parasitic nematodes.
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 | 0.000 | 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".