Net nitrogen mineralization in typical paddy soils of the Taihu Region of China under aerobic conditions: Dynamics and model fitting
Bibliographic record
Abstract
An efficient nitrogen fertilizer recommendation for plant production depends on the amount of N supplied by the soils. A study to investigate the characteristics of net N mineralization in typical paddy soils in an important rice production area of China was conducted on aerobic soils for 147 d of incubation at 25°C. Results showed that the organic nitrogen mineralized ranged from 40 to 360 mg N kg-1 or from 2.92 to 14.17% of total N. In a partial correlation analysis, the N mineralized was only correlated with total N and alkaline hydrolyzable N. Principal component analysis indicated two types of soil physical and chemical properties, each with different influence on N mineralization. Four models: (1) an effective cumulated temperature model (Temperature model), (2) a one-component, first-order exponential model (One-pool model), (3) a two-component, first-order exponential model (Two-pool model), and (4) a two-component, mixed first- and zero-order exponential model (Special model) were fitted to the measured amounts of N mineralized over time using a non-linear regression procedure. All models gave good fits. Model parameters were compared and correlated with the soil basic properties and nitrogen availability indices. All results showed that the Special model performed a better prediction of net nitrogen mineralization in paddy soils under non-flooded conditions than the other models investigated. Key words: Seasonally flooded soil, aerobic net N mineralization, simulation modeling, nitrogen availability indices
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".