Second-generation bioethanol from industrial wood waste of South American species
Bibliographic record
Abstract
There is a global interest in replacing fossil fuels with renewable sources of energy. The present review evaluates the significance of South-American wood industrial wastes for bioethanol production. Four countries have been chosen for this review, i.e., Argentina, Brazil, Chile, and Uruguay, based on their current or potential forestry industry. It should be noted that although Brazil has a global bioethanol market share of 25%, its production is mainly first-generation bioethanol from sugarcane. The situation in the other countries is even worse, in spite of the fact that they have regulatory frameworks in place already allowing the substitution of a percentage of gasoline by ethanol. Pines and eucalyptus are the usually forested plants in these countries, and their industrial wastes, as chips and sawdust, could serve as promising raw materials to produce second-generation bioethanol in the context of a forest biorefinery. The process to convert woody biomass involves three stages: pretreatment, enzymatic saccharification, and fermentation. The operational conditions of the pretreatment method used are generally defined according to the physical and chemical characteristics of the raw materials and subsequently determine the characteristics of the treated substrates. This article also reviews and discusses the available pretreatment technologies for eucalyptus and pines applicable to South-American industrial wood wastes, their enzymatic hydrolysis yields, and the feasibility of implementing such processes in the mentioned countries in the frame of a biorefinery.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".