Influence of crested wheatgrass on soil water repellency in comparison to native grass mix and annual spring wheat cropping
Bibliographic record
Abstract
Crested wheatgrass (CWG) [Agropyron cristatum (L.) Gaertn.] is a commonly introduced grass in the Canadian prairies, but concerns remain about its possible long-term effects on soil quality, and its influence on soil water repellency (SWR) has not been determined. The long-term (24 yr) effects of CWG on SWR in comparison to seeded native grasses and annual cropping were determined for a clay loam soil in southern Alberta, Canada by measuring SOC concentration and SWR using soil hydrophobicity (SH) and soil water repellency index (RI) methods. The cropping treatments were CWG, seeded native grass mix (NGM), continuous wheat, and wheat–fallow rotation, each with fertilized (nitrogen) and non-fertilized subplots, replicated four times. Mean SOC concentration, SH, and RI in samples of surface soil were similar (P > 0.05) for CWG and seeded NGM, and they did not support our hypothesis (seeded NGM > CWG). Mean SOC was significantly greater for seeded perennial grasses than annual crops by 1.7–2 times and SH by 2.1–2.5 times, which supported our hypothesis, but RI was similar among treatments. As expected, nitrogen fertilization significantly increased SOC concentrations, but the effects on SH and RI were undetectable. A strong positive correlation occurred between SOC concentration and SH (r = 0.92) but not for RI (r = 0.10). Our findings suggested that SWR was similar for CWG and seeded NGM. The SWR as measured using SH was greater for seeded perennial grasses than annual cropping but was similar using RI.
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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 | 0.000 | 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".