Compendium of 530 metagenome-assembled bacterial and archaeal genomes from the polar Arctic Ocean
Bibliographic record
Abstract
The role of the Arctic Ocean ecosystem in climate regulation may depend on the responses of marine microorganisms to environmental change. We applied genome-resolved metagenomics to 41 Arctic seawater samples, collected at various depths in different seasons during the Tara Oceans Polar Circle expedition, to evaluate the ecology, metabolic potential and activity of resident bacteria and archaea. We assembled 530 metagenome-assembled genomes (MAGs) to form the Arctic MAGs catalogue comprising 526 species. A total of 441 MAGs belonged to species that have not previously been reported and 299 genomes showed an exclusively polar distribution. Most Arctic MAGs have large genomes and the potential for fast generation times, both of which may enable adaptation to a copiotrophic lifestyle in nutrient-rich waters. We identified 38 habitat generalists and 111 specialists in the Arctic Ocean. We also found a general prevalence of 14 mixotrophs, while chemolithoautotrophs were mostly present in the mesopelagic layer during spring and autumn. We revealed 62 MAGs classified as key Arctic species, found only in the Arctic Ocean, showing the highest gene expression values and predicted to have habitat-specific traits. The Artic MAGs catalogue will inform our understanding of polar microorganisms that drive global biogeochemical cycles. Using genome-resolved metagenomics for 41 Arctic seawater samples, this ecogenomic analysis of 530 metagenome-assembled genomes (MAGs) from the polar Arctic Ocean reveals uncultured Arctic bacterial and archaeal MAGs, their gene expression patterns, habitat preferences and metabolic potential.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".