Coffee Pulp: An Industrial By-product with Uses in Agriculture, Nutrition and Biotechnology
Bibliographic record
Abstract
The coffee shell or pulp is the first by-product obtained from the processing of coffee.It represents approximately 40 to 50% of the coffee berry's weight.Currently, in much of the industry, it is a waste product with a major environmental impact on the water and soil, flora and fauna, and a problem to nearby populations in terms of odor and proliferation of insects and pathogenic microorganisms.This is a review that compiles alternative uses of coffee pulp in agriculture, food and nutrition, medicine and biotechnology.In food and agriculture, for example, the pulp can be used as organic fertilizer to improve degraded soils, in the biological control of plant pathogens, as food or substrate for microorganisms and worms, as feed for chickens, sheep, goats, fish and other animals, and in the productions of foods and beverages for human consumption.In biotechnology, coffee pulp can be used in the cultivation of edible fungi, production of enzymes, substrate for caffeine degrading microorganisms and for microorganisms that produce natural fungicides.Although many of these applications have been proposed and studied, there are also several novel uses that are in the early stages of development; for example, the use of pulp bioactive compounds to make food supplements, or to increase dietary fiber contents in foods and beverages, as well as for the production of biocontainers and biopackaging, alternatives to plastics and their serious environmental impact.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".