Circumpolar biodiversity monitoring program’s state of the Arctic marine biodiversity
Bibliographic record
Abstract
Marine mammals are top predators in Arctic marine ecosystems and are key to ecosystem functioning. Many Arctic marine mammal species are important resources and hold special cultural significance in Arctic communities. The CBMP (Circumpolar Biodiversity Monitoring Programme) Marine Mammal Expert Network aggregated and reviewed data on the population status and trends of all 11 ice-associated marine mammal Focal Ecosystem Components (FECs) across eight Arctic Marine Areas as well as the state of current monitoring (and research) efforts for these species. Changes taking place in the physical environment in the Arctic due to global warming are affecting marine mammal behaviour, abundance, growth rates, body condition and reproduction, and impacting the resilience of marine mammal populations with concomitant effects on the people who rely on them for subsistence, economic and cultural purposes. Effective marine mammal population monitoring will need improved techniques and application at appropriate geographic scales to measure trends that can be evaluated relative to changes in climate (e.g., sea-ice cover) and human activities (e.g., hunting, shipping, mineral exploration). This presentation will summarize current marine mammal monitoring across the Arctic, the status and trends of FECs, drivers of observed trends, and knowledge and monitoring gaps.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".