A Review on Current Status of Biochar Uses in Agriculture
Bibliographic record
Abstract
In a time when climate change increases desertification and drought globally, novel and effective solutions are required in order to continue food production for the world's increasing population. Synthetic fertilizers have been long used to improve the productivity of agricultural soils, part of which leaches into the environment and emits greenhouse gasses (GHG). Some fundamental challenges within agricultural practices include the improvement of water retention and microbiota in soils, as well as boosting the efficiency of fertilizers. Biochar is a nutrient rich material produced from biomass, gaining attention for soil amendment purposes, improving crop yields as well as for carbon sequestration. This study summarizes the potential benefits of biochar applications, placing emphasis on its application in the agricultural sector. It seems biochar used for soil amendment improves nutrient density of soils, water holding capacity, reduces fertilizer requirements, enhances soil microbiota, and increases crop yields. Additionally, biochar usage has many environmental benefits, economic benefits, and a potential role to play in carbon credit systems. Biochar (also known as biocarbon) may hold the answer to these fundamental requirements.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| 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) | 0.008 | 0.003 |
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".