Prediction of Soil Nitrogen Supply in Potato Fields in a Cool Humid Climate
Bibliographic record
Abstract
This study evaluated different strategies for use of a simple first‐order kinetic model (N min = N 0 [1‐e − k t ] where N 0 is potentially mineralizable N and k is the mineralization rate constant) to predict growing season soil N supply (SNS) in potato ( Solanum tuberosum L.) fields under cool humid climatic conditions. Direct application of the kinetic model for the 0‐ to 15‐cm depth significantly underestimated a field‐based measure of plant available soil N supply (PASNS). Modeling strategies that considered the soil mineral N (SMN) present at the start of the growing season, or included a pool of labile mineralizable N (Pool I) not normally considered in determination of N 0 , performed better, but still underestimated high values of PASNS. Strategies which included a greater soil depth (0–30 cm), or which assumed that the mineralizable N pool was replenished during the growing season, overestimated PASNS. A strategy which used a higher value of k for Pool I gave the most promising results. Results of this study highlight the importance of considering both SMN and labile mineralizable N pools in predicting SNS, and suggest that it is possible to estimate growing season SNS in humid regions using simple kinetic models.
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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 | 0.001 |
| 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".