Deposition pattern of precipitation and throughfall in a subtropical evergreen forest in south-central China
Bibliographic record
Abstract
The effects of dry deposition, canopy leaching, precipitation ion concentration, and precipitation H+ concentration on net throughfall flux (NTF, throughfall minus bulk precipitation) were evaluated on a seasonal basis by using a multiple regression analysis approach based on an observation period of 4 years in Shaoshan subtropical mixed evergreen forest, south-central China. Regression analysis results indicated that the estimated canopy exchange flux was the dominant factor regulating the NTF and the calculated dry deposition was a minor term. The seasonal dry deposition of base cations accounted for 15%–43% of the NTF. The NTF analysis showed that K+, Ca2+, Mg2+, Na+, and weak acids in throughfall were derived from foliar leaching and the canopy uptakes of H+, NH4+, and NO3− were from precipitation. The retention rate of proton (H+ and NH4+) in the canopy was close to the canopy leaching rate of base cations when corrected for weak acids because weak acid-induced canopy leaching did not exchange with protons, which suggested that the canopy leaching processes neutralized acid precipitation in Shaoshan forest.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".