Allochthonous carbon is a major regulator to bacterial growth and community composition in subarctic freshwaters
Bibliographic record
Abstract
In the subarctic region, climate warming and permafrost thaw are leading to emergence of ponds and to an increase in mobility of catchment carbon. As carbon of terrestrial origin is increasing in subarctic freshwaters the resource pool supporting their microbial communities and metabolism is changing, with consequences to overall aquatic productivity. By sampling different subarctic water bodies for a one complete year we show how terrestrial and algal carbon compounds vary in a range of freshwaters and how differential organic carbon quality is linked to bacterial metabolism and community composition. We show that terrestrial drainage and associated nutrients supported higher bacterial growth in ponds and river mouths that were influenced by fresh terrestrial carbon than in large lakes with carbon from algal production. Bacterial diversity, however, was lower at sites influenced by terrestrial carbon inputs. Bacterial community composition was highly variable among different water bodies and especially influenced by concentrations of dissolved organic carbon (DOC), fulvic acids, proteins and nutrients. Furthermore, a distinct preference was found for terrestrial vs. algal carbon among certain bacterial tribes. The results highlight the contribution of the numerous ponds to cycling of terrestrial carbon in the changing subarctic and arctic regions.
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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 | 0.001 |
| 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".