Marine distribution of arctic seabirds over six decades: changes and conservation applications
Bibliographic record
Abstract
Climate change is causing rapid changes in Arctic marine ecosystems and understanding its impacts on wildlife is critical for conservation management, especially as the decline in sea ice leads to increased development and vessel traffic. The Arctic supports hundreds of millions of seabirds, which feed upon a wide range of trophic levels among a broad spectrum of marine habitats. Studying the patterns and processes of seabird distributions at sea can identify critical habitats and help to monitor the impacts of climatic and anthropogenic changes. Using two databases of vessel-based survey data collected from 1988 to 2015 and covering a combined 185,000 linear km, we examined the marine distribution of seabirds in sub-arctic and Arctic waters between Canada and Greenland, an area covering over 5,000,000 km2. We developed a predictive model to investigate how ice cover and ocean processes influence the distribution of arctic seabirds in summer and autumn and identified existing areas of high density. Comparing these results to at-sea surveys conducted in the same waters from 1966 - 1987, we examined how seabird distribution has changed over the last six decades. Understanding how changes in the marine environment have led to changes in the marine distribution of seabirds provides the basis from which to predict how seabirds might respond to future changes in the Arctic marine ecosystem, which is particularly important in light of future development.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".