The structure of bacterial communities in the western Arctic Ocean as revealed by pyrosequencing of 16S rRNA genes
Bibliographic record
Abstract
Bacterial communities in the surface layer of the oceans consist of a few abundant phylotypes and many rare ones, most with unknown ecological functions and unclear roles in biogeochemical processes. To test hypotheses about relationships between abundant and rare phylotypes, we examined bacterial communities in the western Arctic Ocean using pyrosequence data of the V6 region of the 16S rRNA gene. Samples were collected from various locations in the Chukchi Sea, the Beaufort Sea and Franklin Bay in summer and winter. We found that bacterial communities differed between summer and winter at a few locations, but overall there was no significant difference between the two seasons in spite of large differences in biogeochemical properties. The sequence data suggested that abundant phylotypes remained abundant while rare phylotypes remained rare between the two seasons and among the Arctic regions examined here, arguing against the 'seed bank' hypothesis. Phylotype richness was calculated for various bacterial groups defined by sequence similarity or by phylogeny (phyla and proteobacterial classes). Abundant bacterial groups had higher within-group diversity than rare groups, suggesting that the ecological success of a bacterial lineage depends on diversity rather than on the dominance of a few phylotypes. In these Arctic waters, in spite of dramatic variation in several biogeochemical properties, bacterial community structure was remarkably stable over time and among regions, and any variation was due to the abundant phylotypes rather than rare ones.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".