Predicting Potential Soil Nitrogen Mineralization Using Pyrolysis-coupled FTIR
Bibliographic record
Abstract
Abstract Soil nitrogen is a key component of plant nutrition, but our ability to predict organic nitrogen mineralization potential remains incomplete. Analytical pyrolysis is an emerging technology used to characterize soil organic matter and the thermal stability of soil carbon. We hypothesized that using pyrolysis to characterize soil nitrogen and measure soil nitrogen release would provide us with a novel method to estimate soil mineralizable nitrogen. A novel online pyrolysis coupled with gas-phase FTIR (Fourier-transform infrared spectroscopy) technology was designed to investigate the thermal stability of soil nitrogen. The soil samples were pyrolyzed at a ramped temperature from 25 to 850 °C at a heating rate of 10 K min− 1, and we followed the pyrogram for ammonia. The temperature at which 50% of the material underwent pyrolysis, referred to as T50, was determined to quantify the thermal stability of organic nitrogen. The T50 was then correlated with potentially mineralizable nitrogen at the end of a 12-week lab mineralization study. A strong negative correlation (R = -0.70, P < 0.01), at a heating rate of 10 K min− 1 was found, linking thermal degradation kinetics and nitrogen mineralization. This research offers a valuable foundation for optimizing pyrolysis applications in the context of understanding and predicting soil organic nitrogen mineralization.
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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) | 0.000 | 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) | 0.001 | 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".