Nitrogen Mineralization Modeling in Some Upland Soils in the Western Region of Thailand
Bibliographic record
Abstract
A laboratory aerobic incubation experiment was conducted to assess the influence of plant residue, soil moisture content, and soil types on mineralization of organic N. The experimental design was a 2 x 3 x 5, factorial arrangement in a Randomized Complete Block design (RCBD) with three replications. The three factors were namely ;(1) two levels of plant residue (0 and 4 ton/rai of grinded cassava shoot), (2) three levels of soil moisture (PWP, ½ AWCA, and FC), and (3) Five upland soil series of the Western region of Thailand, consisting of three main soil textures (fine, medium, and coarse texture). Nitrogen mineralized from native and added organic matter was examined at specific time intervals for 1 year. The data was fitted to a logistic mathematical model describing the relation between mineral N versus moisture content (?), level of plant residue (PR), and time (t). The model was verified by predicting the amount of mineral N released under a given condition and the result was compared to the observed value under the same condition. Application of the model for field condition where soil moisture fluctuates was performed by differentiating the original model to obtain the relation between rate of mineral N production versus N (t), which was the implicit function of ?, PR, and t. Stepwise calculation of the cumulative mineral N with time was developed to predict the amount of N mineralized through time. The results revealed that addition of 4 ton/rai plant residue drastically increased mineral N by 3.6 folds. The effect of soil moisture content and mineralization of organic N confirmed the significance of water on microbial activities. A satisfactory result was obtained from the mathematical model verification. The b and R2 values were close to 1.0 and the t-test were non-significant. A rather high value of RMSE was obtained contributing to the cycles of microbial population fluctuation. Application of the model to the condition of fluctuating soil water content was performed and compared to the observation value at a given level of plant residue application and soil water content.
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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 | 0.001 |
| 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".