Nutrient Fluxes in Rainfall, Throughfall, and Stemflow in <i>Pinus densata</i> Natural Forest of Tibetan Plateau
Bibliographic record
Abstract
This study aimed to measure the chemical components of the rainfall, throughfall, and stemflow, and to evaluate the characteristics of the solutes in the Pinus densata forest in Tibetan plateau. Rainfall, throughfall, and stemflow were monitored from March 2011 to November 2013. The pH and concentrations of Cl − , Na + , K + , Ca 2+ , and Mg 2+ were measured. Depositions of rainfall, throughfall, and stemflow, and canopy budget of different solutes were calculated. There were significant correlations among throughfall, stemflow, and rainfall. The monthly pH of the three forms of water was significantly different between each other. The annual average concentrations of the solutes showed the following order: stemflow>throughfall>rainfall. The annual mean rainfall depositions of rainfall, throughfall, and stemflow were 104.20, 175.54, and 16.60 kg ha −1 · per year, respectively. Annual net throughfall and stemflow deposit was 87.94 kg ha −1 · per year. The main source of and came from rainfall while Cl − , Na + , K + , Ca 2+ , and Mg 2+ input mainly came from the throughfall. Settlement of NH 4 + ‐N was close to the global average, Cl − , K + , Mg 2+ were above the global average, and Na + and Ca 2+ were slightly lower than the global average. The rainfall may have the potential to supply nutrients in the P. densata forest in the Tibetan plateau.
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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 | 0.001 |
| 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".