Exploitation alternatives of olive mill wastewater: production of value-added compounds useful for industry and agriculture
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
Countries producing olive oil generate a considerable amount of olive mill wastewater (OMWW), one of the most harmful agro-industrial effluents with a powerful polluting capacity. In fact, owing to its high pollution load, this effluent is extremely toxic to the whole soil-air-water ecosystem as well as to the living organisms inhabiting it (i.e., plants, animals, aquatic organisms, microorganisms, etc.). Currently, OMWW is discarded but since it includes carbohydrates, organic acids and mineral nutrients, as well as elevated contents of phenolics and other natural antioxidants compounds, it could be considered as a potential source of high value-added natural products. Therefore, the valorization of different waste streams including OMWW into fine biochemicals and the recovery of valuable metabolites via biotechnological processes is probably the main challenge faced by the olive oil industry. In light of that, the aim of the present review article is to summarize the state-of-the-art in relation to the exploitation possibilities and the use of OMWW to generate added-value compounds of great significance for the biofuel, pharmaceutical, cosmetic, chemical, food, and agriculture industries. Valorization of this significant waste steam in particular through a biorefinery platform could substantially enhance the environmental sustainability aspects of the whole industry while simultaneously contributing to the improvement of its economic viability.
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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 | 0.002 | 0.001 |
| 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 | 0.001 |
| 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