Identifying key marine habitat sites for seabirds and sea ducks in the Canadian Arctic
Bibliographic record
Abstract
The Canadian Arctic hosts millions of marine birds annually, many of which aggregate in large numbers at well-defined sites at predictable times of the year. Marine habitats in this region will be under increasing threats from anthropogenic activities, largely facilitated by climate change and long-term trends of reduced sea ice extent and thickness. In this review, we update previous efforts to delineate the most important habitats for marine birds in Arctic Canada, using the most current population estimates for Canada, as well as recent information from shipboard surveys and telemetry studies. We identify 349 160 km 2 of key habitat, more than doubling earlier suggestions for key habitat extent. As of 2018, 1% of these habitats fall within the boundaries of legislated protected areas. New marine conservation areas currently being finalized in the Canadian Arctic will only increase the proportion protected to 13%.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".