Influence Mechanisms of Rainfall and Terrain Characteristics on Total Nitrogen Losses from Regosol
Bibliographic record
Abstract
The upper reach of the Yangtze River is an ecologically sensitive region where water loss, soil erosion, and nonpoint source (NPS) pollution are serious issues. In this drainage area, regosol is the most widely distributed soil type. Cultivation on regosol is extensive and total nitrogen (TN) has become a common NPS pollutant. Artificial rainfall experiments were conducted to reveal the influence mechanisms of rainfall and terrain on TN losses from regosol. The results showed that there were positive correlations between precipitations and TN loads but negative ones between precipitations and TN concentrations. Furthermore, negative correlations were more obvious on fields with slopes of 5° and 25° than on other slopes. With increasing rainfall intensity, TN loads rose simultaneously. However, TN concentration in runoff-yielding time presented a decline over time. As far as terrain was concerned, TN loads grew generally but not limitlessly when slopes increased. Similarly, TN concentrations also rose with rising slopes; upward trends were more obvious for steeper slopes. Furthermore, the initial runoff-yielding time became longer for steeper slopes and the differences under various rainfall intensity conditions diminished gradually.
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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) | 0.001 | 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".