Effects of different fertilizers on soil enzyme activities and soil CO_2 emission under no-tillage on dry land in farming-pastoral zone of northern China
Bibliographic record
Abstract
Soil enzymatic activities and CO2 emission of no-tillage lands with different treatments of fertilizers was determined,analysed and estimated for their effects on soil enzymatic activities and CO2 emission under no-tillage lands in farming-pastoral zone of northern China and their correlations.Valuable data were provided for improving soil quality,enhancing farmland carbon sequestration,reducing CO2 emission and conducting sustainable utilization in dry land region.The results showed: the soil enzymatic activities and CO2 emission in the fertilizer treatments were higher than those from the no-fertilizer treatment under no-tillage.The increased activities of Alkali-phosphates,Ivertase and CO2 emission was mostly influenced by N-fertilizer,followed by P-fertilizer and K-fertilizer while the increased Catalase activities were mainly affected by K-fertilizer.The soil enzymatic activities and CO2 emission was further enhanced by the combined uses of NP-fertilizer or NPK-fertilizer.However,K-fertilizer treatment,compared to N-P fertilizer treatment,showed a better increase on Catalase activity.Conclusively,a highly positive correlation existed between soil CO2 emission and Ivertase and Urease activity while Alkali-phosphates and Catalase activity do not correlate CO2 emission.
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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".