Factors influencing litter decomposition rates in upstream and downstream reaches of river systems of eastern Canada
Bibliographic record
Abstract
In northern Nova Scotia, Canada, we compared litter decomposition rates in cool, woodland brooks and warm, unshaded rivers within the same drainage basins to test the influences of temperature, litter-feeding invertebrates (shredders) and sedimentation on decomposition rates. In the South River system, mass loss rates from N-poor red maple (Acer rubrum) and N-rich speckled alder (Alnus incana) leaf litter confined in mesh bags were measured in the summer and again at autumn leaf-fall. In the summer, decomposition of both species proceeded faster at the cool upstream site, and decomposition of maple litter was faster than N-rich alder. In the autumn, decomposition was much slower in cold water and differences between sites and species disappeared. In the South River system, the total volume of litter-feeding invertebrates (shredders) was greater upstream in summer and downstream in autumn. Movement of benthic sediments did not modify decomposition rates in either season. Summer mass loss experiments with alder leaves on two other river systems produced similar results to those from the South River system. Water temperature appears to have an indirect, negative effect on decomposition rates in summer, because of limitations on distributions of stenothermal shredders, but a direct and positive effect in autumn, when cold water limits biological activity.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".