Compositional relationships between organic matter in a grassland soil and its drainage waters
Bibliographic record
Abstract
Summary We present a novel study of the compositional relationships between soil organic components extractable in aqueous extractants and those in sub‐soil drainage and surface runoff waters from the soil. The surface soil (0–20 cm) of a stagnogley in long‐term grassland was sequentially and exhaustively extracted in aqueous media at pH values of 7, 10.6 and 12.6. Extracts from the soils and their runoff and drainage waters were processed by the procedures of the International Humic Substances Society (IHSS), and fractionated into humic, fulvic, and XAD‐4 acids. Elemental, δ 13 C, δ 15 N, sugar, amino acids, and solid state CPMAS 13 C NMR analyses were used to identify similarities and differences between the fractions from the different extracts. There were few differences between the compositions of drainage water samples taken 1 year apart, and these had compositional features similar to those from the more highly oxidized fractions isolated from the soil at pH 7. There were significant differences between the humic components from the drainage waters and isolated from the soil at pH 7 and those of the humic fractions isolated at the higher pH values whose compositions are more clearly related to origins in plants. The compositions of the surface runoff waters indicate origins in transformed plant and animal manures on the soil surface, whereas those of the deep drainage waters originate in more extensively transformed materials, including products of microbial metabolism. The resin technique used in the fractionation allowed the isolation of novel humic acid fractions from the soil extracts, in particular at pH 7 and 12.6. These fractions clearly originated in microbial sources, were rich in saccharides and amino acids (peptides), and low in lignin‐derived components.
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 | 0.001 | 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".