Specialized Bacteroidetes dominate the Arctic Ocean during marine spring blooms
Bibliographic record
Abstract
Abstract A metagenomic time series from Arctic seawater was obtained from the Canadian region of Dease Strait, to analyse the changes in bacterioplankton caused by the phytoplankton bloom that recurrently occurs in summer. This dataset documents the growth of bacterial clades specialised in the metabolism of plysaccharides, such as Bacteroidetes, along with the phytoplackton. These specialised taxa quickly displaced the microbial clades that dominate nutrient-poor waters during early spring, such as Archaea, Alpha- and Gammaproteobacteria. At the functional level, phyla Bacteroidetes, Planctomycetes and Verrucomicrobia showed higher contents of polysaccharide-degradation functions. Glycoside hydrolases revealed that the Bacteroidetes community shifted towards species with higher polysaccharide-degrading capabilities, targeting algal polysaccharides in summer. Regarding transporters, Bacteroidetes dominated SusC-TonB transporters and had an exclusive family of glycoside-binding proteins (SusD). These proteins were used to identify polysaccharide-utilisation loci that clustered transporters and polysaccharide-active enzymes, showing a higher level of specialisation towards polysaccharide use. Put together, all these genomic features point to the genetic adaptations that promote the dominance of Bacteroidetes during phytoplankton blooms.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".