Effects of rain-shelter cultivation on soil physicochemical properties and kiwifruit yield
Bibliographic record
Abstract
In this study, we investigated the differences in soil physical and chemical properties as well as kiwifruit yield, between rain-shelter cultivation (BY) and open-field cultivation (CK) in Southwest China from 2020 to 2021. The results indicated that the BY treatment significantly improved the nutrient supply capacity of the soil and increased fruit yield. Compared with CK, soil moisture, bulk density, pH, total nitrogen, total phosphorus, total potassium, and organic matter content were lower under the BY treatment, whereas soil conductivity, available nitrogen, available phosphorus, and available potassium were significantly higher. Principal component analysis revealed significant differences in the soil physical and chemical properties between the two cultivation methods at each sampling period. Correlation analysis between yield and soil physical and chemical properties showed that except for pH, all indicators were highly correlated with yield ( R2 > 0.87**). The BY treatment significantly increased yield by enhancing soil contents such as soil available nitrogen, phosphorus, and potassium, while reducing water content and bulk density. However, owing to the increase in soil electrical conductivity, there is a potential risk of salinization. To mitigate this, it is essential to supplement the soil with total nitrogen, total phosphorus, total potassium, and organic matter to maintain soil health.
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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) | 0.000 | 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".