Effects of experimental warming, litter species, and presence of macroinvertebrates on litter decomposition and associated decomposers in a temperate mountain stream
Bibliographic record
Abstract
Small woodland streams make the majority of water courses in most watersheds. Litter decomposition is a key ecosystem process in these shaded streams, and its response to warming can have profound consequences for food webs and the carbon (C) cycle. However, these responses can be modulated by litter identity and the structure of the detrital food web. Here we report on a manipulative study aiming at evaluating the effects of warming (+2.8 °C), litter identity (chestnut (Castanea sativa) or oak (Quercus robur) litter), and the structure of the detrital food web (presence or absence of macroinvertebrates) on litter decomposition and decomposers in a small, temperate woodland stream. Warming significantly stimulated overall (microbial- + macroinvertebrate-driven) decomposition of oak and microbial-driven decomposition of chestnut. The similar shredder densities at elevated and ambient temperatures suggest that stimulated overall decomposition of oak resulted from increased activity of macroinvertebrate individuals. Stimulated microbial-driven decomposition of chestnut resulted from higher fungal activity with warming. Stimulation of litter decomposition by warming can lead to increases in the amount of C returned to the atmosphere and to a faster disappearance of litter from the benthos, with consequences to the C cycle and aquatic food webs.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".