Overview of feedstock research in the United States, Canada, and Brazil
Bibliographic record
Abstract
This is an overview of the current biomass feedstock efforts in Brazil, Canada, and the United States. The report from Brazil provides an historical perspective of incentive programs, the charcoal and fuelwood energy programs, the alcohol program, and other biomass energy efforts. The efforts in Brazil, particularly with the sugar cane to ethanol and the charcoal and fuelwood programs, dwarfs other commercial biomass systems in the Americas. One of the bright spots in the future is the Biomass Integrated Gasification/Gas Turbine Electricity Project initially funded in 1992. The sugar cane-based ethanol industry continues to develop higher yielding cane varieties and more efficient microorganisms to convert the sugar cane carbohydrates into alcohol. In Canada a number of important institutions and enterprises taking part in the economical development of the country are involved in biomass research and development including various aspects of the biomass such as forestry, agricultural, industrial, urban, food processing, fisheries and peat bogs. Biomass feedstock research in the United States is evolving to reflect Department of Energy priorities. Greater emphasis is placed on leveraging research with the private sector contributing a greater share of funds, for both research and demonstration projects. The feedstock program, managed by ORNL, is focused on limited model species centered at a regional level using a multidisciplinary approach. Activities include a stronger emphasis on emerging environmental issues such as biodiversity, sustainability and habitat management. DOE also is a supporter of the National Biofuels Roundtable, which is developing principles for producing biomass energy in an economically viable and ecologically sound manner. Geographical Information Systems are also being developed as tools to quantify and characterize the potential supply of energy crops in various regions.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".