Enzyme kinetics characteristics of soil in the reclaimed homestead land on Loess Plateau
Bibliographic record
Abstract
【Objective】 The objective of this study was to clarify kinetics characteristics of land soil enzyme affected by different fertilizing measures,and develop the reclamation fertilization system.【Method】 The field experiments with 6 fertilizing treatments(no fertilizer,fertilizer,compound fertilizer,compound fertilizer+bacterial manure,organic fertilizer,organic fertilizer+bacterial manure)were conducted to study the kinetics characteristics(Km,Vmax and Vmax/Km)of soil invertase,urease,and alkaline phosphatase in the Changwu County,Shaanxi Province.【Result】 Compared with the initial soil samples,chemical fertilizer reduced Vmax of soil urease and Vmax and Vmax/Km of phosphatase and compound fertilizer reduced Vmax/Km of urease.Compound fertilizer combined with bacterial manure reduced Vmax and Vmax/Km of urease as well as Vmax and Vmax/Km of phosphatase,while organic fertilizer combined with bacterial manure and organic fertilizer increased Vmax and Vmax/Km of invertase,urease and phosphatase.Correlation and principal component analysis showed that Vmax of invertase,Km and Vmax of urease,Vmax of phosphatase were important factors for evaluation of soil fertility.【Conclusion】 The research showed that single organic fertilizer or combined with bacterial manure was a rational fertilization to improve fertility of the homestead land soil on Loess Plateau.
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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".