Oil Cakes as Sustainable Agro‐Industrial Feedstock for Biocarbon Materials
Bibliographic record
Abstract
Abstract The demand for vegetable oil is increasing for both food and non‐food applications, which leads to the generation of a huge amount of oil cakes. The rising annual production requires alternative applications for sustainable operations of oil mills, especially those involved in non‐edible oil production. Hence, the value‐added uses of oil cakes such as environment remediation (metal absorption), composite fabrication (as fillers, reinforcements), nanoparticle synthesis (as reducing and stabilizing agent), and production of carbonaceous materials (as carbon source) were extensively explored in recent years. Among them, the thermochemical conversion of oil cakes into carbonaceous materials (biochar and activated carbon) received great interest as the demand for biocarbon materials increases exponentially. Oil cake‐derived biocarbon materials found a wide range of technological applications. With this perspective, recent developments in oil cake‐derived carbon materials and their diverse applications are reviewed.
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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 | 0.001 | 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) | 0.002 | 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".