Significant decline observed in Arctic Tern Sterna paradisaea population in northwest Greenland
Bibliographic record
Abstract
Arctic Terns Sterna paradisaea are an understudied species in Greenland. Outside of the largest colonies, little is known about their current population status and no long-term annual monitoring program has been established. Previous surveys outside of key breeding colonies have been limited to a small number of colonies for single-season studies. This limits our understanding of population dynamics as terns have been shown to have a high degree of variation in annual reproduction and readily move between nearby colonies. Here we present results of the first multi-year systematic survey for breeding Arctic Terns in the High Arctic of northwest Greenland. Surveys took place from 2009 to 2017 and identified eight islands where terns currently nest. A total of 426 nests were counted and annual counts ranged from 25 to 92 nests. Complete reproductive failure in at least one year was noted for seven of the eight colonies, and three colonies comprised nearly 90% of the total population. Mean clutch size ranged from 1.4 to 1.7 eggs. When compared with historical data from the late 1960s through late 1990s, the population has declined by over 50%, with the greatest decline occurring over the past two decades. Four previously identified colonies have been extirpated while three colonies have undergone significant reductions in size.
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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 | 0.000 |
| 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) | 0.000 | 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".