Seed Hull Utilization
Bibliographic record
Abstract
Oilseed, pulse, and cereal grain crops have been used as food and feed sources for millennia. Recently, many crops such as wheat, corn, and soybean have been investigated as biofuel sources. To fulfill the growing global demand for both food and fuel, the seed processing industry has rapidly expanded as has the volume of waste. According to FAO estimates, 1.3 billion tons of food are wasted each year including about 20% of total seed production. This waste in the food chain has social, environmental, and economic impacts and does not align with the current endeavor to move toward a more sustainable and eco-friendly society. Seed processing wastes include hull or husk, leaf, peel, pomace, skin, rind, core, pit, pulp, and stem. Such remnants can be reservoirs for recovery of valuable materials, chemicals, and compounds including protein, carbohydrates, lipids, and small molecules. Recent strategies for more sustainable waste management include approaches for total seed utilization. New techniques are emerging to achieve preservation of valuable compounds from source to final product for subsequent separation and utilization. More efficient and less invasive dehulling methods and extraction processes with “eco-friendly” solvents such as supercritical CO2, solid-state fermentation, anhydrous ethanol and subcritical water extraction are good examples of such emerging techniques. New analytical methods and state-of-the-art technology can enable recovery and valorization of usually discarded seed hull materials that can be used to produce new products for industrial, food, feed, nutraceutical, and pharmaceutical applications. This chapter includes an overview of seed hull recovery strategies and potential applications of the recovered components.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.039 | 0.028 |
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".