Environmental, social, and economic benefits of biochar application for land reclamation purposes
Bibliographic record
Abstract
Biochar is a solid material produced by pyrolysis of biomass, which was shown to improve soil properties. On the other hand, there are a number of risks and uncertainties associated with its use in land reclamation. This case study is aimed to assess environmental, social, and economical benefits and limitations of biochar use for revegetation projects in northern Saskatchewan. Four revegetation options were examined, i.e. natural restoration, revegetation with peat application, and revegetation with application of commercially or locally produced biochar. The assessment methods included option screening by the expert panel, stakeholder opinion survey, and quantitative assessment (i.e. screening life cycle assessment and life cycle costing analysis). The study results suggest that biochar provides a number of environmental benefits and its on-site production can also provide social benefits and economic opportunities. On the other hand, biochar production and application is expensive and associated with technical risks, which can undermine overall project success. Nevertheless, positive trends in biochar production industry suggest that in the near future this material may serve as an affordable and technically reliable alternative to conventional soil amendments for land reclamation.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".