Spent Coffee Grounds Characterization and Reuse in Composting and Soil Amendment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As an everyday beverage, coffee is consumed worldwide, generating a high amount of waste after brewing, which needs attention for its disposal. These residues are referred to as spent coffee grounds (SCGs), which have been shown to have applications as polymers/composites precursors, biofuels, and biofertilizers. This review focuses on agricultural applications usually based on organic matter to fertilize the soil and consequently improve plant growth. To date, SCGs have been shown to exhibit outstanding performance when applied as soil amendment and composting because it is a nutrient-rich organic waste without heavy metals. Therefore, this review presents the different options to use SCGs in agriculture. First, SCG composition using different characterization techniques is presented to identify the main components. Then, a review is presented showing how SCG toxicity can be resolved when used alone in the soil, especially at high concentrations. In this case, SCG is shown to be effective not only to enhance plant growth, but also to enhance nutritional values without impacting the environment while substituting conventional fertilizers. Finally, a conclusion is presented with openings for future developments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it