Landscape position, sampling time, and tillage, but not legume species, affect labile carbon and nitrogen fractions in a 4-yr-old rejuvenated grazed pasture
Bibliographic record
Abstract
Termination by tillage is one strategy used for regenerating pasture stands. Yet, research gaps exist on how tillage affects carbon (C) and nitrogen (N) forms and amounts in western Canadian soils. We measured total soil organic C (SOC), dissolved organic C (DOC), total dissolved N (TDN), light fraction organic C (LFOC) and light fraction organic N (LFON), microbial biomass C (MBC) and microbial biomass N (MBN), and inorganic N as indicators of soil organic matter (SOM) dynamics. After tillage termination in fall 2018, we sampled soils (0–10 cm; 0–15 cm) under three legume species (alfalfa, cicer milkvetch, and sainfoin) three times (spring, summer, and fall of 2019) across three landscape positions. Legume species did not affect the measured parameters. Over time, tillage affected DOC, TDN, and inorganic N. Averaged across three pasture legumes and three landscape positions, tillage increased DOC 29% by summer. Fall-applied tillage led to 59% and 33% higher TDN in the succeeding summer and fall. Inorganic N increased by 14% and 40% across landscape positions and sampling after tillage. Averaged across landscape positions, MBC decreased by 31% from spring to summer and increased by 51% from summer to fall. However, MBN increased by 53% and decreased by 5% within the same period. The seasonal fluctuations in MBC and MBN reflected variations in moisture, temperature, and substrate quality. Total SOC, LFOC, and LFON increased on the upper slopes and fall sampling time. Although single intensive tillage did not affect total SOC, several tillage operations could accelerate SOM loss and reduced total C storage over time.
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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 | 0.001 | 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".