Organic Matter Additions for Improved Revegetation of Arsenic-Rich Waste Rock with Planted Boreal Conifers: A Three-Year In Situ Monitoring Study
Bibliographic record
Abstract
Abstract Mining waste creates challenging soil conditions that hinder tree establishment for boreal forest restoration. This study investigated the effects of adding topsoil or ramial chipped wood (RCW) on the physicochemical properties of waste rock and the growth and survival of planted native tree species. An randomized-block setup with four treatments and repetitions was established on a gold mine site in western Quebec, Canada in 2018, and planted with Pinus banksiana and Abies balsamea. Results demonstrated that topsoil addition significantly improved height and diameter growth, aerial and root biomasses, survival, and nutrient uptake (N, P, and S) in conifer seedlings, concomitantly to increased water content, decreased pH, and elevated nutrient concentrations in the substrate. However, multivariate analysis revealed that these improved soil conditions alone did not determine the survival and growth of conifer seedlings. In contrast, the application of RCW-based treatments had no discernible impact on the growth and survival of the planted trees. Additionally, topsoil addition effectively reduced the concentration of potentially phytotoxic elements in soil and needles, particularly arsenic. The total arsenic concentration in the mineral substrate (84.1 to 507 µg.g− 1) emerged as a growth-limiting factor for both conifer species. The total concentration of arsenic in the waste rock correlated positively with arsenic accumulation in the tree needles, indicating potential root uptake of this element. This study emphasizes the significance of addressing arsenic availability during reclamation efforts at mine sites. Nonetheless, further research is required to determine the phytotoxic thresholds of arsenic on conifers and its potential metabolic 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".