Leaching of base and metal cations from litter and soils vary in two forest stands with different tree species
Bibliographic record
Abstract
Understanding the distribution of cations in forest soils is important for forest management. Here, we evaluated the leaching of cations, potassium (K+), sodium (Na+), calcium (Ca2+), magnesium (Mg2+), iron (Fe3+), aluminium (Al3+), and manganese (Mn2+), from litter through soils in two forest stands with different tree species. We incubated Castanopsis carlesii leaf litter in a Castanopsis carlesii stand and Cunninghamia lanceolata needle litter in a Cunninghamia lanceolata stand using a microcosm method with monthly collections of litter and soil leachates, and the concentrations of cations and fluxes of these cations were assessed separately. We found more Ca2+ but less Na+, Mg2+, and Fe3+ fluxes in litter leaching solutions in Cunninghamia lanceolata than in Castanopsis carlesii stand because of their different initial concentrations in fresh litter. Although cations leached from leaf litter differed among tree species, the leaching fluxes did not vary between stands. Moreover, annual fluxes of cations leached from soils were significantly higher than those from leaf litter, leading to a net loss of soil nutrients to downstream environment. Therefore, the results suggest that reforestation with mixed stands by introducing broadleaved trees in Chinese fir monoculture plantations might reduce soil nutrient loss through the leaching pathway.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".