Impaired cellulose decomposition in a headwater stream receiving subsurface agricultural drainage
Bibliographic record
Abstract
Background: Agricultural development of former wetlands has resulted in many headwater streams being sourced by subsurface agricultural drainage systems. Subsurface drainage inputs can significantly influence stream environmental conditions, such as temperature, hydrology, and water chemistry, that drive ecological function. However, ecological assessments of subsurface drainage impacts are rare. We assessed the impact of an agricultural drainage system on cellulose decomposition and benthic respiration using a paired stream study in a headwater branch of Nissouri Creek, in Ontario, Canada. Adjacent first order segments sourced by a spring-fed marsh and a cropped field with subsurface drainage, as well as the adjoining trunk segment, were sampled over a year using the cotton strip assay to measure cellulose decomposition and benthic respiration. Results: Assessments of cellulose decomposition revealed a one-third reduction in the drainage-sourced segment compared to marsh-sourced segment. Between segment differences in cellulose decomposition were associated with reduced summer temperatures in the drainage-sourced segment. Impacts of stream cooling from the drainage-sourced segment were transmitted downstream as cellulose decomposition was slower than expected throughout the drainage-sourced segment and for several hundred meters down the adjoining trunk segment. Benthic respiration only differed between the drainage- and marsh-sourced segments in spring, when stream temperatures were similar. Conclusions: Our findings suggest there may be a widespread reduction in cellulose decomposition in streams across similar agricultural regions where subsurface drainage is prevalent. However, cooling of streams receiving significant amounts of water inputs from subsurface drainage systems may impart increased resiliency to future climate warming.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".