Benthos Expert Network: Findings and recommendations from the Circumpolar Biodiversity Monitoring Program’s State of the Arctic Marine Biodiversity Report (SAMBR)
Bibliographic record
Abstract
Currently, > 4,000 macro- and megabenthic invertebrate species are known from Arctic seas, representing the majority of marine faunal diversity in this region. This estimate is expected to increase with future studies. Benthic invertebrates are important ecosystem components as food for fishes, marine mammals, seabirds and humans. The Benthos Expert Network of the Circumpolar Biodiversity Monitoring Program (CBMP) aggregated and reviewed information on the population status and trends of macro- and megabenthic invertebrates across eight Arctic Marine Areas as well as the state of current monitoring efforts for these communities. Drivers are affecting benthic communities on a variety of scales, ranging from pan-Arctic (related to climate change, such as warming, ice decline and acidification) to regional or local scales (such as trawling, river/glacier discharge, and invasive species). Long-term benthic monitoring efforts have largely focused on macro- and megabenthic communities of the Chukchi and Barents Seas. Recently, they are increasing in waters off Greenland and Iceland, as well as in the Canadian Arctic and the Norwegian Sea. All other Arctic Marine Areas are lacking long-term monitoring. The presentation will summarize current level of knowledge and monitoring across the Arctic, 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 | 0.021 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 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) | 0.029 | 0.023 |
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".