Unique archaeal assemblages in the Arctic Ocean unveiled by massively parallel tag sequencing
Bibliographic record
Abstract
The Arctic Ocean plays a critical role in controlling nutrient budgets between the Pacific and Atlantic Ocean. Archaea are key players in the nitrogen cycle and in cycling nutrients, but their community composition has been little studied in the Arctic Ocean. Here, we characterize archaeal assemblages from surface and deep Arctic water masses using massively parallel tag sequencing of the V6 region of the 16S rRNA gene. This approach gave a very high coverage of the natural communities, allowing a precise description of archaeal assemblages. This first taxonomic description of archaeal communities by tag sequencing reported so far shows that it is possible to assign an identity below phylum level to most (95%) of the archaeal V6 tags, and shows that tag sequencing is a powerful tool for resolving the diversity and distribution of specific microbes in the environment. Marine group I Crenarchaeota was overall the most abundant group in the Arctic Ocean and comprised between 27% and 63% of all tags. Group III Euryarchaeota were more abundant in deep-water masses and represented the largest archaeal group in the deep Atlantic layer of the central Arctic Ocean. Coastal surface waters, in turn, harbored more group II Euryarchaeota. Moreover, group II sequences that dominated surface waters were different from the group II sequences detected in deep waters, suggesting functional differences in closely related groups. Our results unveiled for the first time an archaeal community dominated by group III Euryarchaeota and show biogeographical traits for marine Arctic Archaea.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".