An investigation of drum granulation of post‐pyrolysis washed biochar
Bibliographic record
Abstract
Abstract Biochar, unwashed and washed with a solution of Triton and hydrogen peroxide, was wet drum granulated using molasses binder solutions. Unwashed biochar was very hydrophobic and granulation proceeded through forming liquid marbles and layering. Washing reduced the hydrophobicity of the biochar. The effectiveness of the wash depended on the biochar source; it significantly reduced the hydrophobicity of biochar from woodchips and moderately reduced the hydrophobicity of biochar from flower digestate. Therefore, washed biochar from woodchips was granulated using a hydrophilic mechanism, while washed biochar from digestate was granulated according to a combination mechanism of liquid marbles collapsing and then coalescing. The change in granulation mechanism produced stronger and denser granules with higher yields of granules in the 1–4 mm optimal size range. Washing and then granulating biochar created a product that could be further tailored for optimal soil amendment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".