Biochar: A sustainable tool for soil health, reducing greenhouse gas emissions and mitigating climate change
Bibliographic record
Abstract
The transformation of agricultural waste into biochar that is both eco-friendly and cost-effective is not only a wise recycling strategy but also a solution to environmental pollution management. Due to its low cost, high efficiency, simplicity of use, ecological sustainability, and reliability in terms of public safety, biochar from agricultural residues can be a useful alternative technique for controlling contaminants. Biochars have achieved significant progress in the following areas: reducing greenhouse gas emissions, reducing soil nutrient dispersion, sequestering atmospheric carbon into the soil, increasing agricultural productivity, and reducing the bioavailability of environmental contaminants. A comprehensive scientific assessment of the relationship between the properties of biochars and their impact on soil properties, environmental pollutant remediation, plant growth, yield, and resistance to biotic and abiotic stresses is warranted by recent advancements in the understanding of biochars. The primary factors influencing biochar's properties are the feedstock nature, heat transfer rate, residence duration, and pyrolysis temperature. The efficacy of biochar in the management of pollutants is significantly influenced by its elemental composition, ion-exchange capacity, pore size distribution, and surface area, which are contingent upon the nature of the feedstock, preparation conditions, and procedures. The chapter investigated the potential of biochar derived from agricultural refuse as a viable alternative for the long-term application of biochar in the environment, soil conditioning, and the remediation of environmental pollutants.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".