Seabird Expert Network (CBird): Findings and recommendations from the Circumpolar Biodiversity Monitoring Program’s State of the Arctic Marine Biodiversity Report
Bibliographic record
Abstract
Seabirds provide ecosystem services, notably as human food in many Arctic regions, major tourist attractions, as well as being an important link to the Arctic food web and returning nutrients from the oceans to coastal areas. Changes in seabird populations and diversity will affect regional sustainability for Arctic communities and ecosystems. The CBird Expert Network aggregated and reviewed data on the population status and trends of eight seabird Focal Ecosystem Components (FECs) across eight Arctic Marine Areas as well as the state of current monitoring efforts for these species. Population trends for seabirds vary within and among regions, making it difficult to assess circumpolar trends. Nonetheless, among key sites, current trends indicate that most of the stable or increasing populations are in the Pacific Arctic and Arctic Archipelago, while most of the declining populations are in the Atlantic Arctic. Most circumpolar nations have at least one source of long-term seabird monitoring datasets, but efforts vary across regions. Long-term monitoring efforts are crucial to examining the effects of environmental drivers to changes in seabird populations. The presentation will summarize current level of monitoring across the Arctic, the status and trends of FECs, drivers of observed trends, and knowledge and monitoring gaps.
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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 | 0.022 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".