Influence of a one-time biosolids application on elemental and nutrient concentrations on mine tailings
Bibliographic record
Abstract
Mine tailings are nutrient deficient, contain no organic matter, and have high metal concentrations preventing the long-term establishment of vegetation. The use of organic amendments, including biosolids, can help the revegetation of mine tailings by adding organic matter and nutrients but can increase metals in the receiving soil. There is also uncertainty if biosolids provide a long-term benefit, or if the benefits diminish with time. To test this, a study was conducted on two tailings storage facilities — a sand, and a silt loam — for a copper mine in the southern interior of British Columbia, Canada. In 1998, biosolids were applied at rates between 50 and 250 Mg·ha−1 and compared with a control and fertilizer treatment. Plots were sampled in 2000 and 2015 for total and available nutrients and metals. This study showed that 17 yr after a one-time biosolids application at different rates, the tailing storage facilities that received biosolids had higher carbon, nitrogen, phosphorus, and biomass compared with tailings that did not receive biosolids or received conventional fertilizer. Many elements such as carbon (C) did not change from 2000 to 2015 in biosolids-treated plots, indicating a long-term benefit to the tailings. Additionally, biosolids did not result in increased concentrations of metals above the national regulatory limits for agriculture. This study suggests that a one-time biosolids application can provide a long-term benefit to tailings, whereas proper application rates can reduce the risks of metal exceedances.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".