Experimental evidence reveals impact of drought periods on dissolved organic matter quality and ecosystem metabolism in subalpine streams
Bibliographic record
Abstract
Abstract Subalpine streams are predicted to experience lower summer discharge following climate change and water extractions. In this study, we aimed to understand how drought periods impact dissolved organic matter (DOM) processing and ecosystem metabolism of subalpine streams. We mimicked a gradient of drought conditions in stream‐side flumes and evaluated implications of drought on DOM composition, gross primary production, and ecosystem respiration. Our experiment demonstrated a production and release of DOM from biofilms and leaf litter decomposition at low discharges, increasing dissolved organic carbon concentrations in stream water by up to 50%. Absorbance and fluorescence properties suggested that the released DOM was labile for microbial degradation. Dissolved organic carbon mass balances revealed a high contribution of internal processes to the carbon budget during low flow conditions. The flumes with low discharge were transient sinks of atmospheric CO 2 during the first 2 weeks of drought. After this autotrophic phase, the metabolic balance of these flumes turned heterotrophic, suggesting a nutrient limitation for primary production, while respiration remained high. Overall our experimental findings suggest that droughts in subalpine streams will enhance internal carbon cycling by transiently increasing primary production and more permanently respiration as the drought persists. We propose that the duration of a drought period combined with inorganic nutrient availability are key variables that determine if more carbon is respired in situ or exported downstream.
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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 | 0.000 | 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) | 0.001 | 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".