Possibilities for the application of agro-industrial wastes in cementitious materials: A brief review of the Brazilian perspective
Bibliographic record
Abstract
Brazil is a country of continental dimensions and characteristics with enormous biodiversity of fauna and flora which confers a prominent role in the sector of extraction of agricultural products. However, one of the current challenges is the increasing amounts of agricultural solid wastes generated by different local production processes which end up resulting in enormous environmental liabilities. One way to effectively manage these agro-industrial wastes is by their application in the development of alternative cementitious materials such as mortars and concretes. Thus, the objective of this paper is to discuss the recent advances, challenges and future perspective of the application of some solid agro-industrial wastes generated specifically in Brazil and some other parts of the world in cementitious materials. The application of wastes from pineapple, sugar cane, açai, coconut and rice were explored and discussed. The discussion presented in this paper is anticipated to strongly contribute to the advancement of public policies that enable the real application of these wastes in the development of eco-friendly cementitious materials for civil construction.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".