Optimizing sugarbeet molasses distillery slops to olive mill husks ratio and incubation period for composting
Bibliographic record
Abstract
This paper analyzes the influence of operating conditions, such as mixing ratio of the waste ingredients and extension of the stabilization time, on the composting of sugarbeet molasses distillery slops (DS) and olive mill husks (OMH). The addition of 040% sugarbeet molasses DS to the starting mixtures and the treatment duration (40 d) were tested at the bench to optimize the process dynamics using the in-vessel composting method. Changes of compost stabilization conditions (organic matter, nitrogen losses, germination index) were also related to the characteristics of the different end products obtained. A second-order polynomial equation based on two independent process variables was developed to model the composting of sugarbeet molasses DS OMH mixtures. Differences between experimental values and data derived by the application of the model never exceeded 10%. The best results in terms of biodegradation rate (i.e., degree of stabilization), maturity, and limitation of Kjeldahl-N losses were observed with process times of 3040 d and the addition of 010% sugarbeet molasses DS to OMH. Key words: biodegradability, compost, sugarbeet molasses distillery slops, olive mill husks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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 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".