Rainfall pulse primarily drives litterfall respiration and its contribution to soil respiration in a young exotic pine plantation in subtropical China
Bibliographic record
Abstract
Although litterfall respiration (RL) is a key process of soil carbon dynamics in forests, factors that drive RL and its contribution to soil respiration (RS) have not been sufficiently studied. Using a litter removal method, we researched RL and the ratio of RL:RS in a 20-year-old exotic slash pine ( Pinus elliottii Englem.) plantation in subtropical China. Soil temperature explained 67%, 78%, and 25% of variation in RS, mineral soil respiration (RS-L), and RL, respectively, but had little impact on RL:RS. To study influences besides temperature, measured RS and RS-L were normalized using the Arrhenius equation. Even though this subtropical plantation was characterized by a humid climate with abundant precipitation, a rainfall pulse induced increase in soil moisture primarily drove RL and its contribution to RS but depressed RS-L. The response of RL to rainfall and soil moisture was significantly more sensitive than that of RS-L. Furthermore, the effects of rainfall and soil moisture on RS, RL, and RL:RS were higher during the dry season (July–December) than during the wet season (January–June). In the context of climate change, RL and its contribution to RS are expected to decrease because of the predicted decrease in rainfall amount and frequency in subtropical regions.
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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) | 0.000 | 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".