The Effect of Biochar Amendment, Microbiome Inoculation, Crop Mixture and Planting Density on Post-Mining Restoration
Bibliographic record
Abstract
Ecological restoration with a multispecies and multifunctional approach can accelerate the re-establishment of numerous ecosystem services. The challenges with land that is degraded, damaged, or destroyed post-mining are the low productivity of soil and the high potential for contaminants. Herein, we evaluated the multispecies and multifunctional approach to restoration strategy through a mixture of woody and herbaceous species, microsymbiont and biochar amendments, and plant spacing. The experiments were conducted using greenhouse and field trials located in Quebec, Canada. We used a mixture of tree species (Alnus viridis (Chaix) DC. ssp. crispa (Aiton) Turrill, Picea glauca (Moench) Voss, Populus tremuloides Michx. and Salix arbusculoides Andersson) and herbaceous species (Avena sativa L., Festuca rubra L. and Trifolium repens L.) on two types of gold-mine waste materials (fine tailing and waste rock). The biochar amendment and microbial inoculation were applied on both greenhouse and field trials. We found both positive and negative effects of plant spacing, biochar amendment and inoculation depending on their interactions. The net positive effect was shown by combining high plantation density, biochar, and inoculation factors on Alnus viridis ssp. crispa. Overall, plantation density was shown to be the most important factor in generating the net positive effect. We suggest that the mechanism was correlated with the improvement in microclimate through soil plant water conservation and microbial activity enhancement over soil temperature modification. Hence, we propose to put emphasis on microclimate improvement for accelerating the restoration processes, along with other combined factors, including microbial inoculation and biochar amendment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".