Carburants diesel renouvelables dérivés de la ligine pour le transport ferroviaire
Bibliographic record
Abstract
This project has been designed by CanmetENERGY-Ottawa (CE-O, Natural Resources Canada) and its industrial partner CRB Innovations Inc. to assess the feasibility of sing lignin-derived diesel fuels in order to reduce the emissions of non-biogenic greenhouse gases and criteria air contaminants from the rail sector. These “drop-in” biofuels are hydrocarbon-based blending stocks fully compatible with conventional diesel fuels. CE-O and CRB Innovations Inc. have been working together on the development of technologies to convert lignin (a major component of wood) into renewable fuels under a multi-year task-shared agreement. CE-O develops and carries out the catalytic hydrotreatment whereas CRB deconstructs and fractionates lignocellulosic biomass and catalytically depolymerizes the lignin-rich fraction producing clean “oligomeric lignin feedstock” used by CE-O. In this project, the focus is to verify whether diesel fuel blends, containing lignin-derived diesel, can meet CGSB 3.18 specifications for locomotive fuel and then perform preliminary exhaust emission tests.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".