Bacterial diversity and lipid biomarkers in sea ice and sinking particulate organic material during the melt season in the Canadian Arctic
Bibliographic record
Abstract
The estimation of important carbon fluxes in a changing Arctic environment remains a challenge, one that could benefit from the development of biomarkers that distinguish between sympagic (ice-associated) and pelagic organic material. Products of 10S-DOX-like lipoxygenase and fatty acid cis-trans isomerase (CTI) activity of bacteria attached to sympagic particulate organic matter (POM) were proposed previously as potential biomarkers of the contribution of sympagic biota to carbon fluxes to the seafloor. To date, neither the bacteria involved in such enzymatic activities nor the detection of these potential biomarkers at their presumed source (i.e., sea ice) has been investigated. Here, we determined and compared the diversity of prokaryotic communities (based on operational taxonomic units) attached to sea ice POM and under-ice sinking particles during an early stage of ice melt (brine drainage) in Baffin Bay (Canadian Arctic). Based on a time series of biodiversity analyses and the quantification of lipid tracers of these two bacterial enzymatic activities, we suggest that CTI-active bacteria, exposed to hypersaline stress, are attached to algal POM just above bottom sea ice and released into the water column following brine drainage. In contrast, bacteria attached to sinking particles and exhibiting 10S-DOX-like lipoxygenase activity are suggested to come from the bottommost layer of sea ice, where they may play a role in the detoxification of algae-produce free fatty acids. These results provide a refined view of the potential use of products of CTI activity as specific biomarkers of sympagic organic matter.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".