Effects of Elemental Sulfur on Soil pH and Growth of Saskatoon Berry (Amelanchier alnifolia) and Beaked Hazelnut (Corylus cornuta) Seedlings
Bibliographic record
Abstract
The land disturbed by open-pit oil sands mining must be restored to support the survival and growth of native boreal plants. Because tailings sand and sodic shale overburden are commonly used as an underlying parent substrate that is capped by boreal forest cover soils, the soil pH in reclamation sites is often higher compared with undisturbed boreal forest soil. Sulfur is a major byproduct of oil sands refining and could potentially be used as an amendment to lower the soil pH on reclamation sites. In this study, we examined the effects of soil pH and elemental sulfur on growth and physiological responses in Saskatoon berry and beaked hazelnut seedlings. We found that elemental sulfur was effective in lowering soil pH. However, addition of elemental sulfur to a forest soil of pH 5.7 lowered the soil pH to around 3, which impaired the growth and physiological performance of both plant species. The addition of 5 and 25 g kg−1 elemental sulfur to the pH 8.5 soil did not substantially improve the examined growth and physiological parameters in Saskatoon berry and beaked hazelnut seedlings. Further, excess addition of elemental sulfur in high pH soil could reduce the uptake of nitrogen, phosphorus, and calcium in Saskatoon berry. The results demonstrate that the amount of sulfur applied to the soil would need to be carefully determined for different soil types and pH levels to avoid potential toxicity effects.
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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 | 0.001 |
| 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".