Metaproteomics of aquatic microbial communities in a deep and stratified estuary
Bibliographic record
Abstract
Here we harnessed the power of metaproteomics to assess the metabolic diversity and function of stratified aquatic microbial communities in the deep and expansive Lower St. Lawrence Estuary, located in eastern Canada. Vertical profiling of the microbial communities through the stratified water column revealed differences in metabolic lifestyles and in carbon and nitrogen processing pathways. In productive surface waters, we identified heterotrophic populations involved in the processing of high and low molecular weight organic matter from both terrestrial (e.g. cellulose and xylose) and marine (e.g. organic compatible osmolytes) sources. In the less productive deep waters, chemosynthetic production coupled to nitrification by MG-I Thaumarchaeota and Nitrospina appeared to be a dominant metabolic strategy. Similar to other studies of the coastal ocean, we identified methanol oxidation proteins originating from the common OM43 marine clade. However, we also identified a novel lineage of methanol-oxidizers specifically in the particle-rich bottom (i.e. nepheloid) layer. Membrane transport proteins assigned to the uncultivated MG-II Euryarchaeota were also specifically detected in the nepheloid layer. In total, these results revealed strong vertical structure of microbial taxa and metabolic activities, as well as the presence of specific "nepheloid" taxa that may contribute significantly to coastal ocean nutrient cycling.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".