Green chemistry, sustainable agriculture and processing systems: a Brazilian overview
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| 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