The use of microalgal sourced biodiesel to help underground mines transition to battery electric vehicles
Bibliographic record
Abstract
The use of fossil fuel sourced diesel underground has various associated health and environmental hazards, and additional energy demand and costs associated with necessary ventilation. One way to reduce these impacts is by utilizing a biodieselblend, which generates lower levels of harmful emissions from underground equipment and can be produced regionally, reducing the impact of transportation. Furthermore, this would help allow use of existing machinery during transition towards more widespread electrification underground. Therefore, the concept of an integrated supply and use chain within the mining industry is examined based on biodiesel from acidophilic photosynthetic microalgae cultivated using CO 2 in smelter off-gas. A life cycle assessment (LCA) was conducted to compare the environmental impacts of production, transportation, and end-use of fossil fuel sourced diesel to biodieselblended fuel across four underground metal ore mine sites (Canada, Poland, Zambia, and Australia). The outcomes from assessing four key environmental impact potentials (global warming, eutrophication, acidification and human toxicity) demonstrate the advantages of using biodiesel-blends. The integration of biodiesel resulted in changes of -22.5–+22.8% (global warming), -6.1–+27.3% (eutrophication), -18.9–+26.3% (acidification), and -21.0–-3.6% (human toxicity). The results showed reduction across all potentials for two mines and reduction in human toxicity potential for all sites.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".