Advancing modified biochar for sustainable agriculture: a comprehensive review on characterization, analysis, and soil performance
Bibliographic record
Abstract
Biochar is a carbon-rich material produced through the pyrolysis of various feedstocks. It can be further modified to enhance its properties and is referred to as modified biochar (MB). The research interest in MB application in soil has been on the surge over the past decade. However, the potential benefits of MB are considerable, and its efficiency can be subject to various influencing factors. For instance, unknown physicochemical characteristics, outdated analytical techniques, and a limited understanding of soil factors that could impact its effectiveness after application. This paper reviewed the recent literature pertaining to MB and its evolved physicochemical characteristics to provide a comprehensive understanding beyond synthesis techniques. These include surface area, porosity, alkalinity, pH, elemental composition, and functional groups. Furthermore, it explored innovative analytical methods for characterizing these properties and evaluating their effectiveness in soil applications. In addition to exploring the potential benefits and limitations of utilizing MB as a soil amendment, this article delved into the soil factors that influence its efficacy, along with the latest research findings and advancements in MB technology. Overall, this study will facilitate the synthesis of current knowledge and the identification of gaps in our understanding of MB.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".