Sea ice presence is linked to higher carbon export and vertical microbial connectivity in the Eurasian Arctic Ocean
Bibliographic record
Abstract
Arctic Ocean sea ice cover is shrinking due to warming. Long-term sediment trap data shows higher export efficiency of particulate organic carbon in regions with seasonal sea ice compared to regions without sea ice. To investigate this sea-ice enhanced export, we compared how different early summer phytoplankton communities in seasonally ice-free and ice-covered regions of the Fram Strait affect carbon export and vertical dispersal of microbes. In situ collected aggregates revealed two-fold higher carbon export of diatom-rich aggregates in ice-covered regions, compared to Phaeocystis aggregates in the ice-free region. Using microbial source tracking, we found that ice-covered regions were also associated with more surface-born microbial clades exported to the deep sea. Taken together, our results showed that ice-covered regions are responsible for high export efficiency and provide strong vertical microbial connectivity. Therefore, continuous sea-ice loss may decrease the vertical export efficiency, and thus the pelagic-benthic coupling, with potential repercussions for Arctic deep-sea ecosystems.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".