Temporal and spatial distributions of soil nutrients in Hani terraced paddy fields, Southwestern China
Bibliographic record
Abstract
Hani terraced paddy fields are one of the most important ways for agricultural products and greatly influence regional landscapes in mountainous areas of Southwestern China. However, the knowledge of soil nutrient conditions from Hani terraced paddy fields is limited. This paper investigates such soil nutrient parameters as organic matter (OM), total nitrogen (TN), total phosphorus (TP), available phosphorus (AP), total potassium (TK), available potassium (AK) of four sampling sites of paddy fields under special geographical environment and agricultural technology, and compares the differences of soil nutrients related to spatial patterns and temporal periods. Correlation analysis is performed to analyze the impact of environmental factors on soil nutrients, as well as the relationships between soil nutrient parameters and altitude, slope direction, gradient and distance from village. The results show that there were some differences separately in the content of soil nutrients such as OM, TN, TP, AP, TK and AK. The AK and AP levels are lower in the fallow period than that in the tillage period, only OM level in the fallow period is higher than that in the tillage period; TN, TK, TP levels are nearly similar in the tillage and the fallow period. Unlike great differences in two periods, soil nutrient content in the ridge of fields is identical basically with the content in the corresponding paddy fields. Correlation analysis shows that soil nutrients of AK, TP, TN and OM have distinctive negative correlations with distance from villages, while AP and TK display a slight fluctuation.
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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".