Bibliographic record
Abstract
Currently, > 4,000 Arctic macro- and megabenthic species are known, representing the majority of Arctic marine faunal diversity. This estimate is expected to increase. \n• Benthic invertebrates are food to shes, marine mammals, seabirds and humans, and are commercially harvested. \n• Traditional Knowledge (TK) emphasizes the link between the benthic species and their predators, such as walrus, and their signi cance to culture. \n• Decadal changes in benthos biodiversity are observed in some well-studied regions, such as the Barents Sea and Chukchi Sea. \n• Drivers related to climate-change such as warming, ice decline and acidification are affecting the benthic community on a pan-Arctic scale, while drivers such as trawling, river/glacier discharge and invasive species have signficant impact on regional or local scales. \n• Increasing numbers of species are moving into, or shifting, their distributions in Arctic waters. These species will outcompete, prey on or offer less nutritious value as prey for Arctic species. \n• Current monitoring efforts have focused on macro- and megabenthic species, but have been confined to the Chukchi Sea and the Barents Sea. Efforts are increasing in waters of Greenland, Iceland, the Canadian Arctic, and in the Norwegian Sea. All other Arctic Marine Areas are lacking long-term benthic monitoring. \n• As a first step towards an international collaborative monitoring framework, we recommend to develop a time- and cost-effective, long-term and standardized monitoring of megabenthic communities in all Arctic regions with regular annual groundfish assessment surveys. Expanding monitoring on micro-, meio- and macrobenthic groups is encouraged.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.118 | 0.074 |
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".