Biorefinery Design: Aligning Feedstocks Supply Chains
Bibliographic record
Abstract
Biofuels have the potential to make a significant contribution to greenhouse gas (GHG) reduction goals while creating long-term operating jobs and energy security. However, the appropriate conversion technology for biofuel production is highly dependent upon feedstock availability, feedstock properties, desired fuel properties, availability of supporting infrastructure, and fuel transportation systems. These technologies, associated in four platforms (pyrolysis, gasification, bioconversion, and emerging techniques) in the BioFuelNet Canada NCE, have shown many benefits with respect to the different available feedstocks. For example, these technologies can treat either dry (pyrolysis, gasification) or wet biomass (hydrothermal liquefaction, bioconversions). [...] In order to overcome several of the major project hurdles, we are proposing to exploit potential synergies between the platforms by using the concept of flexible modular biorefinery. Analogous to oil refineries, such biorefineries should integrate the emerging technologies to treat, on the same site, different feedstocks and the subsequently reused organic wastes. Therefore, by aligning feedstocks, supply chains, and production technologies and by treating wastes, a flexible modular biorefinery could generate affordable biofuels for a wide range of use.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 0.002 |
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".