Contrary effects of flow intermittence and land uses on organic matter decomposition in a Mediterranean river basin
Bibliographic record
Abstract
Flow interruption in intermittent rivers (IRs) generates a mosaic of terrestrial and aquatic habitats across the river network affecting ecosystem processes, as organic matter (OM) decomposition. Water use for farming in arid and semi-arid climates intensifies the dry conditions and affects local river characteristics. In that way, flow intermittence and the distribution of land uses may affect the OM processing along the river. To understand the role of IRs in global OM dynamics and how global change affecting water flow regimes determines these dynamics, it is important to estimate OM-processing rates at a basin scale. The aim of this study was to evaluate the effect of the intensity of flow intermittence on OM processing, and how this effect was modulated by local environmental factors related to land uses across a Mediterranean river basin. To do this, wood decomposition (mass loss and fungal biomass) was selected as a functional indicator. Drying duration and frequency were measured to characterize flow intermittence in different reaches along the river, as well as local environmental factors. Linear models stablished the role of factors on decomposition. The results showed that differences in decomposition rates across the river network were negatively related to the duration of flow interruption. Dissolved inorganic nitrogen associated with agriculture counteracted the negative effect of intermittence on mass loss (increasing by up to three times); but with a higher duration of dry conditions, its effect was insignificant. An increase of 20% of canopy (higher in natural areas) resulted in increases of up to 5% of mass loss. Overall, our study is relevant to understanding the interaction between flow intermittence and land uses on OM processing, especially considering the intensification of flow intermittence and its increased distribution to other regions, which is expected to be a consequence of climate warming and human activities.
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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 | 0.000 |
| 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) | 0.000 | 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".