Deep tillage reduces the dependence of tobacco (<i>Nicotiana tabacum</i> L.) on arbuscular mycorrhizal fungi and promotes the growth of tobacco in dryland farming
Bibliographic record
Abstract
The traditional shallow tillage method reduces soil quality and affects the efficiency of agricultural production. Using conventional rotary tillage (12 cm) as the control, Yunyan 87 as the test variety, and paddy soil as the test site, we studied the effects of deep tillage (subsoiling 30 cm) on soil nutrients, arbuscular mycorrhizal fungi (AMF), and tobacco (Nicotiana tabacum L.) growth. The results showed that deep tillage increased the content of organic carbon, available phosphorus (AP), and available potassium (AK) in the 20–40 cm soil layer. The AMF community was also affected by deep tillage. Glomus, the dominant genus in both groups, increased significantly in soil after deep tillage. The AMF colonization rate was lower than that of conventional rotary tillage. Deep tillage was beneficial for tobacco growth in the middle and late stages. The root growth and nutrient content of the tobacco plants increased. Deep tillage significantly improved the output value of tobacco plants. Deep tillage is conducive to improving soil fertility, promoting the vigorous growth of roots, reducing the dependence of tobacco on AMF, and promoting the high quality and yield of tobacco in the drylands of Hunan.
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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".