Disturbance Drives Leaf Litter Leachate Dynamics in a Tropical Stream Ecosystem
Bibliographic record
Abstract
Abstract Tropical rainforests in many regions are experiencing an increased frequency of severe hurricanes and droughts due to climate change, which can alter the quantity and quality of organic matter inputs entering tropical freshwater ecosystems through inputs of leaf litter. This study leached dried senesced and freshly abscised leaves in a controlled laboratory setting as proxies of drought‐ and hurricane‐induced changes to leaf litter inputs, respectively. The nine species that were leached are representative of the dominant riparian vegetation across most of the Luquillo Mountains of Puerto Rico. Leachate analytics, including forms of carbon, nitrogen, and major cations and anions, were analyzed across leaf condition and species to assess relationships between climatic events, species type, and leaf leachate composition. Total accumulation of solutes and concentrations of dissolved organic matter and major ions were about 2–4 times higher in leachate from dried senesced leaves (i.e., drought litter inputs) than freshly abscised leaves (i.e., hurricane litter inputs); however, the magnitudes of these differences were highly variable across species, potentially connected to leaf tissue chemistry. These data allow for scaling the impact of riparian leaf litter inputs to further our understanding of the biogeochemical and metabolic response of tropical streams to increasingly frequent climatic disturbances.
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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".