Bibliographic record
Abstract
Surface mining activities cause severe adverse effects on soils. Scientists across the world have used different physical, chemical and biological reclamation techniques to recover mining disturbed areas. The effectiveness and efficiency of reclamation techniques is crucial to reclamation success. Biochars are biological residues combusted under low oxygen conditions, resulting in a porous, low density carbon rich material. Research has suggested that biochar can be used as an amendment to improve soil physical, chemical, and biological quality. The present study investigated the application of biochar as a soil amendment for land reclamation. Specifically, the impact of biochar application on aspen growth, microbial biomass, soil respiration, heavy metal adsorption, and metabolic quotient were measured in a greenhouse experiment using land reclamation soils and in a field experiment on a reclaimed coal mine west of Edmonton, AB, Canada. Results of the greenhouse experiment showed that the biochar had the ability to retain the soil nutrients, increase the soil microbial biomass and soil heterotrophic respiration; while the petroleum- coke had a negative impact on tree growth. In the field experiment, the results showed that biochar increased DOC, DON (dissolved organic carbon and nitrogen), MBC and MBN (microbial biomass carbon and nitrogen) and soil heterotrophic respiration. The results are consistent with previous findings which suggested that biochar can improve soil available nutrient and increase microbial activity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 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".