Long-term agricultural land use affects chemical and physical properties of soils from Southwest Saskatchewan
Bibliographic record
Abstract
Understanding nutrient cycling under different land uses can improve agricultural management practices. In southwestern Saskatchewan, long-term land use as annual cropland, native grassland pasture, tame (planted) crested wheatgrass grasslands, or roadsides altered soil physical and chemical properties based on the intensity and frequency of disturbance, with cropland > roadsides > tame grassland > native grassland. The majority of significant differences were detected at the soil surface (0–7.5 cm); few significant differences below 15 cm suggested that the soils were not significantly different prior to changes in land use. Bulk density was increased in cropland soils compared with native grassland, probably from compaction from farm equipment, and in tame pastures due to their past use as croplands. Croplands also had decreased carbon and organic phosphorus (P) and increased Olsen P compared with grasslands, from crop removal and fertilizer inputs. Roadsides, an important but poorly studied land use in Saskatchewan, had increased clay and Olsen P concentrations compared with native grassland. Roadsides were disturbed during road building and remained disturbed because of runoff from adjacent fields and dust from roads. These results on soil chemical and physical properties, combined with soil microbiology information, will help to improve land management and nutrient use efficiency in soils of this region.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".