Cruising for Data: Defining the Seabird Community from Vessels of Opportunity in Canada’s Eastern Arctic
Bibliographic record
Abstract
Information on marine bird abundance and distribution at sea is required to identify important habitat for protection, mitigate pressures from human activities, and understand the role of seabirds in marine food webs. Arctic waters support millions of marine birds, including globally significant numbers of some species, but the remote location coupled with the financial costs of research and monitoring in this region limit our ability to quantify marine habitat use. We used standardized survey data collected from vessels of opportunity during 2007-2023 to describe the distribution and abundance of marine birds in eastern Canadian Arctic waters and to examine the relative contribution of data collected from two primary platform types: research vessels and cruise ships. Northern Fulmars Fulmarus glacialis, Thick-billed Murres Uria lomvia, Black-legged Kittiwakes Rissa tridactyla, and Dovekies Alle alle accounted for 92% of the sightings. The survey area covered by research vessels was 3.5 times greater than that covered by cruise ships, but there was minimal (< 1%) spatial overlap between the two platform types. Cruise ships travelled closer to shore and in shallower water than research vessels, including areas close to major colonies during the breeding season, which resulted in higher densities of birds observed. In addition to providing access to unique survey areas, cruise ships presented opportunities to engage tourists in the process of science and the outcomes of biodiversity monitoring programs. Large-scale monitoring programs that include boat-based surveys from a variety of platform types and collaboration among multiple organizations will remain important for defining marine bird habitat use in an area where human impacts are increasing as sea ice cover declines.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".