Machine learning for sustainable reutilization of waste materials as energy sources – a comprehensive review
Bibliographic record
Abstract
This work reviews Machine Learning applications in the sustainable utilization of waste materials as energy source so that analysis of the past works exposed the lack of reviewing study. To solve it, the origin of waste biomass raw materials is explained, and the application of Machine Learning in this section is scrutinized. After analysis of numerous papers, it is concluded that Machine Learning and Deep Learning are widely utilized in waste biomass production areas to enhance the quality and quantity of production, improve the predictions, diminish the losses, as well as increase storage and transformation conditions. The positive effects and application with the utilized algorithms and other effective information are collected in this work for the first time. According to the statistical analysis, in 20% out of the studies conducted about the application of Machine Learning and Deep Learning in waste biomass raw materials, Artificial Neural Network (ANN) algorithm has been applied. Afterward, the Super Vector Machine (SVM) and Random Forest (RF) are the second and third most-utilized algorithms applied in 15% and 14% of studies. Meanwhile, 27% of studies focused on the applications of Machine Learning and Deep Learning in the Forest wastes.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 teacher head, 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".