Rhizosphere soil affects pear fruit quality under rain-shelter cultivation
Bibliographic record
Abstract
The use of rain shelters in pear cultivation has been shown to improve yields and the appearance and quality of fruit, as well as reduce diseases and pests; however, how rain shelters affect soil chemical properties, soil enzyme activity, and soil microbial diversity remains unknown. Here, we studied pear trees under rain-shelter cultivation and open-field cultivation in the same orchard and compared fruit quality, soil chemical characteristics, soil enzyme activity, and soil microbial diversity. Results showed that rain shelters can significantly (p < 0.05) increase the sugar content (sweetness) of pear fruits and decrease the content of acids. The levels of available phosphorus, available potassium, organic matter, and water in soils under rain shelters were significantly (p < 0.05) lower than in soils in open fields. Rain-shelter treatment increased soil polyphenol oxidase activity and decreased phosphomonoesterase, urease, and sucrase activity. Analysis of microbial carbon-source utilization rates and microbial diversity showed that open-field cultivation is beneficial for microbial carbon-source utilization and microbial diversity in rhizosphere soil. Our study found that rain-shelter cultivation is not beneficial to soil fertility, microbial carbon-source metabolism and utilization, matter cycling, or microbial diversity and that the use of rain shelters may require appropriate nutrient and organic matter supplementation to maintain long-term cultivation of crops; whereas, the effects of environmental factors on open-field cultivation are greater, and more refined water and fertilizer management is required to improve fruit quality.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".