Exchangeable cation effects on hot water extractable carbon and nitrogen in agricultural soils
Bibliographic record
Abstract
Hot water is believed to extract bio-available soil organic matter (SOM), including organic compounds from the biomass of soil microbes. The role of soil physico-chemical factors in relation to extractability of SOM in hot water is not well understood. We evaluated the influence of exchangeable sodium (Na) on the quantity and quality of organic matter extracted in hot water from soils with a range of total and extractable C (total C 19–60 g kg–1; hot water extractable C (HWC) 659–3292 mg kg–1). The soils were pre-treated with different rates of Na (0–156 cmolc kg–1, as NaCl) to establish a range of exchangeable Na percentages (ESP), and then extracted with hot water (80°C) for 16 h. Hot water extractable C increased linearly as ESP increased, but the rate of increase differed between soils (the increase in HWC per unit increase in ESP ranged within 19–71 mg kg–1). At ESP 15, a threshold used to separate sodic and non-sodic soils, HWC was 30–60% greater than that measured without added Na. Ultraviolet absorbance (260 nm) data indicated that aromatic organic matter was preferentially released following Na pre-treatment. The proportion of HWC in phenolic form was generally little affected by Na treatment but there was a consistent increase in protein in response to Na (hot water extractable organic N in protein form increased from an average of 5.5 ± 2.2% without added Na to 11.0 ± 3.6% at the highest Na rate). The Na-induced increases in UV absorbance may be largely attributable to release of proteins containing aromatic amino acids (tryptophan and tyrosine). Our results suggest that organic matter desorbed from mineral surfaces may be an important contributor to hot water extractable C and N, and factors that affect the adsorption–desorption process may significantly influence organic matter extractability in hot water.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".