One-time application of biosolids to ungrazed semiarid rangelands: 14 yr soil responses
Bibliographic record
Abstract
Biosolids have been shown to improve forage production and soil quality on semiarid rangelands in the short term, but less is known about longer-term impacts of one-time biosolids applications. The objective of this study was to determine the effects of a single, surface biosolids application (at 20 dry Mg ha−1) on stability of soil aggregates, bulk density, total carbon (C) and nitrogen (N), permanganate-oxidizable carbon (POXC), polysaccharides, pH, nutrient availability, and soil water content (all at 0–7.5 cm depth) 14 yr following application to ungrazed rangelands in the Central interior of British Columbia. Fourteen years following the biosolids application, aboveground plant biomass was almost two times greater with biosolids application than on control, while exposed mineral soil and microbiotic crust significantly decreased in biosolids plots. Despite differences in aboveground biomass, there was no difference in total soil C and N, POXC, and polysaccharides between biosolids and control plots. Biosolids-amended soil did exhibit significantly greater mean weight diameter of water-stable aggregates, lower pH, increased spring soil water content, and increased availability of Fe3+, Zn2+, Cu2+, and phosphate ions. These findings indicate that the long-term improvements to soil on ungrazed rangeland are possible even from a single biosolids application.
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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) | 0.001 | 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 | 0.001 |
| 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".