Impacts of one time biosolids and fertilizer application on long-term metal and nutrient concentrations on two tailings ponds in the BC Southern Interior
Bibliographic record
Abstract
Previous research has demonstrated that the use of organic amendments, specifically biosolids, can address limitations to initial vegetation establishment on mine tailings. It is less understood how these systems will function in the long term. In 2015, a field study at Teck Highland Valley Copper in the BC Southern Interior was conducted to determine the long term effects of fertilizer and biosolids on nutrients and elemental concentrations in two tailings ponds. Seventeen years prior, biosolids were applied in a randomized complete block design at rates 50, 100, 150, 200, and 250 Mg haˉ¹. The biosolids treatments continue to demonstrate a very clear increase to the nutrient status of the tailings, while the fertilizer treatment does not statistically differ from the control treatments. There are also still elevated levels of metals within biosolids treated plots, but results vary by metal with many showing a plateau, where additional biosolids do not increase their concertation. With site specific planning, metal concentrations can be controlled below levels of concern, while at the same time promote nutrient cycling. In conclusion, it appears a one-time biosolids application can assist reclamation in a trajectory towards a self-sustaining state. Further research is also being done examining the plant community and soil development.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".