Determining the Stability of Sugarcane Filtercake Biochar in Soils with Contrasting Levels of Organic Matter
Bibliographic record
Abstract
Sugarcane filtercake is a nutrient-rich residue produced prior to sugarcane distillation and is commonly disposed of by applying directly to agricultural fields, often causing high decomposition and leaching rates. Transforming this material into biochar could improve its stability in the soil. In this 92-day incubation study, filtercake biochar produced at 400 °C (BC400) and 600 °C (BC600) was used to trace biochar stability when mixed with two soils with different organic matter levels: an agricultural field (1.2% carbon (C)) and a forest (2.8% C) soil. Based on δ13C isotope analysis, biochar decreases in the field soil mostly occurred in the coarse silt fraction. In contrast, biochar decreases in forest soil appeared to be more equally distributed in all particle size fractions. A negative priming effect in biochar-amended soils was noticeable, mainly in the forest soil. Cumulative CO2 emissions were greater in soils with BC400 than in those with BC600 for both field and forest soils, while adding biochar increased CO2 emissions only in field soils. This increase did not appear to affect native soil organic matter pools. High-temperature filtercake biochar could thus be a more stable alternative to the current practice of raw filtercake applications.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".