Detecting change in seabird distributions at sea in arctic and sub-arctic waters over six decades
Bibliographic record
Abstract
In the western North Atlantic and eastern Arctic, data on the distribution and abundance of seabirds at sea have been collected by the Canadian Wildlife Service from two main survey programs using ships of opportunity. The first, PIROP (Programme intégré de recherches sur les oiseaux pélagiques) collected quantitative information on seabird occurrence from 1965-1992 and the second, ECSAS (Eastern Canada Seabirds at Sea) from 2006-present. Combining the ECSAS data with data collected off the west coast of Greenland from 1988-2015 by the Danish Centre for Environment and Energy, we developed predictive models to investigate how ice cover and ocean processes influence the distribution thick-billed murre (Uria lomvia), northern fulmar (Fulmarus glacialis), dovekie (Alle alle), and black-legged kittiwake (Rissa tridactyla) in summer and autumn between Canada and Greenland. We used the PIROP data to examine how the distribution of these four species has changed over the last six decades. We discuss the results in relation to ocean climate variability, but also the challenges that exist when comparisons span such long time periods, including monitoring programs with changing priorities, differences in data-collection methodologies, and advances in technologies that are difficult to apply to historic datasets.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".