Effects of Precipitation and Topography on Total Phosphorus Loss from Purple Soil
Bibliographic record
Abstract
The Sichuan Basin is the main agricultural production area of the upper reaches of the Yangtze River and is also an extremely important ecological area because it is rich in biodiversity and has complex and diverse landscape types. The dominant soil type, purple soil, is prone to rapid soil erosion and weathering processes because it is shallow and rich in phosphorus and other nutrients. Field experiments were conducted to reveal the effects of precipitation and topography characteristics on nonpoint source pollutants from purple soil. The results showed that total phosphorus (TP) load and TP concentration both increased with increasing rainfall amount, and there was an initial time of runoff and sediment yield before runoff generation. Moreover, the TP load generally increased with precipitation intensity as setting a coincident value of rainfall amount; however, the difference between TP load at 30 and 60 mm/h was minimal as was the difference between 90 and 120 mm/h. Similarly, TP concentration increased with increasing precipitation intensity. In terms of topographical conditions, TP load increased with increasing gradient, but began to decline when the gradient was about 20°, which indicates that 20° is the critical gradient for TP loss. There was a significant positive correlation between gradient and TP concentration when the gradient was <15°, whereas the increase in TP concentration slowed as the gradient increased.
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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 | 0.000 | 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 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".