Plant response to biochar, compost, and mycorrhizal fungal amendments in post‐mine sandpits
Bibliographic record
Abstract
Extreme growing conditions inhibit restoration in sandpit mines. Co‐amendment of soil conditioners such as biochar, compost, and arbuscular mycorrhizal fungi (AMF) may alleviate these stresses and lead to a more successful restoration. We conducted a multiyear restoration experiment in a sandpit in Southern Ontario, Canada, following industrial‐scale grassland restoration protocols. The sandpit substrate was sand with low carbon (C) and nutrients. We tested the effect of biochar, compost, and AMF inoculum in two experiments (plant plugs vs. seed application). In the plant plug trial, we investigated the treatment effects on the growth of eight grassland plant species and colonization of plant roots by AMF over two growing seasons. We found that co‐amending soils with compost plus biochar (20 T/ha + 10 T/ha) was more beneficial than other amendment combinations. Amendments including AMF were not more beneficial to plant growth than those without AMF. In the seed application trial, direct inoculation of AMF in the field combined with high compost addition (20 T/ha or 40 T/ha) resulted in the highest plant cover compared to other treatment combinations. Our results indicate that co‐amending sandpit substrates with biochar, compost, and AMF are practical restoration tools that enhance grassland restoration.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".