Particle-associated extracellular enzyme activity and bacterial community composition across the Canadian Arctic Ocean
Bibliographic record
Abstract
Microbial enzymatic hydrolysis of marine-derived particulate organic carbon (POC) can be a dominant mechanism for attenuating carbon flux in cold Arctic waters during spring and summer. Whether this mechanism depends on composition of associated microbial communities and extends into other seasons is not known. Bacterial community composition (BCC) and extracellular enzyme activity (EEA, for leucine aminopeptidases, glucosidases and chitobiases) were measured on small suspended particles and potentially sinking aggregates collected during fall from waters of the biologically productive North Water and river-impacted Beaufort Sea. Although other environmental variables appeared influential, both BCC and EEA varied along a marine productivity gradient in the two regions. Aggregates harbored the most distinctive bacterial communities, with a small number of taxa driving differences between particle-size classes (1.0-60 and > 60 μm) and free-living bacteria (0.2-1.0 μm). Significant relationships between patterns in particle-associated BCC and EEA suggest strong links between these two variables. Calculations indicated that up to 80% of POC in the euphotic zone of the North Water, and 20% in the Beaufort Sea, may be hydrolyzed enzymatically, underscoring the importance of this mechanism in attenuating carbon fluxes in Arctic waters even as winter approaches.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| 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".