Green chemistry, sustainable agriculture and processing systems: a Brazilian overview
Bibliographic record
Abstract
Abstract There is a pressing need for renewable and optimal use of resources towards sustainable primary production and processing systems worldwide. Current technologies for food and feedstock production are held accountable for several environmental problems, such as for instance soil and water contamination due to the use of hazardous substances, generation of toxic products and even excess of biomass that is considered waste. To minimize or solve these questions in order to produce an adequate quantity of reliable and healthy food, fibers and other products and energy, new paradigms focusing on sustainable agriculture, bio-based industries or biorefineries have emerged over the last decades. Biorefineries integrate sustainable and environmentally friendly concepts of Green Chemistry with intelligent and integrated farming processes, optimizing the agricultural production. Thermochemical and biochemical processes are excellent alternatives for the production of new classes of renewable biofuels and feedstock, showing relatively small impact on greenhouse gas emissions and important pathways to obtain platform chemicals. This review discusses the current and incipient technological developments for using biomass to generate bio-based chemicals over the last decade, focusing on Green Chemistry concepts towards sustainable agriculture and processing models in Brazil.
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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 | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".