Leaf‐litter microbial communities in boreal streams linked to forest and wetland sources of dissolved organic carbon
Bibliographic record
Abstract
Abstract Leaf‐litter microbial activity is influenced by several stream characteristics that may be affected by alterations in watershed condition. However, there have been few studies and little direct evidence that leaf‐litter microbial communities are affected by disturbance‐induced watershed condition, particularly in boreal streams. To test this linkage, we compare the associations of stream physical–chemical characteristics with landscape features (e.g., percent wetlands, roads, riparian woody stem diversity), and leaf‐litter microbial activity and structure in streams across varying disturbance‐induced watershed conditions. Our findings suggest that the increased stream water conductivity associated with roads can have a negative impact on leaf‐litter microbial extracellular enzyme activity associated with a decrease in the abundance of Betaproteobacteria. Wetlands and forests in contrast are important providers of dissolved organic carbon that stimulates the microbial, and in particular fungal, cycling of energy and nutrients. We present a novel and in‐depth perspective of leaf‐litter microbial communities as a critical link to our understanding and management of the influences of watershed condition on aquatic ecosystems.
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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 | 0.000 |
| 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) | 0.001 | 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".