Method to support the biofuel supplier choice: A LCA approach
Bibliographic record
Abstract
Many countries like the US, Brazil, Australia, Canada and some European countries have proposed policies to encourage the use of biofuels by the implementation of regulations to encourage their use in the different sectors of the economy. Among them we find the public transport sector. It is still difficult to assess the potential environmental and social benefits of the use of biofuels because they are distributed throughout the supply chain. The way to supply can influence the level of environmental and social impacts. It is possible to observe a gap in supply decision models taking into account the environmental and social impacts in the case of chains of biofuels that are already established. In the case of biodiesel used by a public transport company, its use should represent the maximum of environmental and social benefits. It is therefore possible to put our research question: “What is the ideal way to supply biodiesel to transportation companies considering environmental and social dimensions?”. Our research goal is to provide an evaluation model to support the biodiesel supplier choice decision-making. We expect that the results of our study support the decision making process related to the biodiesel supply for transport companies in Quebec.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".