Bibliographic record
Abstract
Abstract Under the Kyoto Protocol, the Government of Canada has committed to reducing its greenhouse gas emissions by 6% from 1990 levels between 2008 and 2012. Ethanol‐blended gasolines have the potential to contribute significantly to these emission reductions. Ethanol is derived from biologically renewable resources and can be employed to replace octane enhancers and aromatic hydrocarbons or oxygenates. To date, the ethanol production industry in Canada is comprised mainly of small‐scale plants producing ethanol primarily from agricultural crops as feedstock. Research interests in the area of bioethanol production from organic waste materials emerged in the late 1980. Significant advances in lignocellulosic material extraction and enzymatic hydrolysis have been reported in the last decade, however, continued research efforts are essential for the development of technically feasible and economically viable large‐scale enzyme‐based biomass‐to‐ethanol conversion processes. This research aims to develop and test an enzyme‐based biomass‐to‐ethanol conversion process, which employs organic waste materials, such as livestock manures, as alternative sources of cellulosic material feedstock. The source of the livestock manure, manure management practices and cellulose extraction procedures have a significant impact on the quantity and quality of the cellulosic materials derived. As such, raw feedstock materials must be carefully characterized to assess the impact of these factors on the yield of bioethanol and residual end products. The success of cellulose‐to‐ethanol conversion processes for cellulose extracted from these waste materials as feedstock is generally a function of cellulose fiber pretreatment, enzyme selection and operating conditions. These will differ depending on the source of the waste material feedstock. The long‐term benefits of this research will be to introduce a sustainable solid waste management strategy for a number of livestock manure and other lignocellulosic waste materials; contribute to the mitigation in greenhouse gases through sustained carbon and nutrient recycling; reduce the potential for water, air, and soil contamination associated with land disposal of organic waste materials; and to broaden the feedstock source of raw materials for the ethanol production industry. © 2007 American Institute of Chemical Engineers Environ Prog, 2008
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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 | 0.000 | 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 | 0.000 | 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".