Carbon and nitrogen contents of different-sized light fraction organic matter as influenced by tillage and residue management
Bibliographic record
Abstract
The light fraction (LF) has a variable elemental content because it comprises a pool of soil organic matter that is in transition between fresh residues and stable, humified organic matter. Our aim was to assess the influence of time, tillage (CT vs. NT) and straw management (removed or retained) practices on the C and N contents of two particle sizes of LF materials from a Gray Luvisol in Alberta. The LF C and N concentrations were not affected by tillage and straw treatments. The C concentration was higher in LF > 1 mm (coarse LF) than in the < 1 mm LF (fine LF), while the converse was observed for N concentration, resulting in C:N ratios of 45-59 in the coarse fraction and 18-19 for the finer materials. The C concentration of the fine LF decreased and the N concentration increased with time. After 4 yr, LF C and N stocks were higher under NT than under CT mainly because of faster decomposition of litter under CT. Retaining straw resulted in bigger increases in C and N stocks in the coarse LF compared with straw removal; with the fine LF, the C stock decreased more quickly and the N stock increased less rapidly with straw removal. Our results show that time strongly affected the LF C and N stocks and concentrations, and that separating the fraction by size can lead to a more meaningful interpretation of those data.Key words: Light fraction, carbon, nitrogen, tillage, crop residue, straw management
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 | 0.001 | 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".