Adsorption of dissolved nitrogen by forest mineral soils
Bibliographic record
Abstract
Dissolved nitrogen (N) species, including ammonium (NH4+-N), nitrate (NO3–-N), and dissolved organic nitrogen (DON), are important nutrient sources in soils. The mobility of DON and NH4+-N in soil solutions is influenced by abiotic adsorption to mineral soil particle surfaces. We determined relationships between soil properties and their ability to adsorb NH4+-N, NO3–-N, and DON, using batch experiments. A range of 41 mineral horizons was collected from across Canada, including Podzols, Brunisols, Luvisols, Gleysols, and an organic soil. The adsorption of DON and dissolved organic carbon (DOC) were positively correlated (R2= 0.86), and both were best explained by the amount of iron and aluminum associated with poorly crystalline phases. Adsorption of NH4+-N was strongest in Gleysols and Luvisols, while adsorption of NO3–-N was weak in all soils. A reduction in the DOC:DON ratio was observed for most mineral horizons (89%) after equilibration from an original ratio of 35:1. Mineral horizons with a net desorption of DON had a significantly greater reduction in the final DOC:DON ratios than horizons with a net adsorption of DON. The results from this study found that while similar soil properties could predict DOC and DON adsorption, DON was slightly more mobile than DOC.
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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 | 0.001 | 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".