CanmetMINING diesel and BEV field test series: MacLean Engineering diesel and battery electric cassette truck
Bibliographic record
Abstract
In light of Canada’s goal of achieving net-zero emissions by 2050 and conditions in increasingly deeper mines, the trend in Canadian mines is to move away from conventional internal combustion engine vehicles and toward battery electric vehicles (BEVs). However, the limited driving range and the longer time required to recharge a battery than refuel a tank could reduce BEV availability and negatively affect production targets. Understanding the differences between these two technologies is critical when designing a new mine or transforming an existing fossil fuel-based fleet into an electric fleet. Thus, the primary objective of this study was to compare diesel cassette trucks (DCTs) and electric cassette trucks (ECTs) in terms of net fuel and energy consumption, respectively. MacLean Engineering heavy-duty DCTs and ECTs were field-tested at Vale’s North Mine surface ramp at 5 and 15 km/h and loaded with the same weight. The controlled 2.5-km test route comprised 10 sections of 0, 5, 10, and 20% uphill and downhill inclination grades. This paper compares DCT and ECT performance in terms of ability to maintain the target speed under different operational conditions and fuel and energy consumption. The energy captured through regenerative braking and charging information was also evaluated for the ECT. An energy to fuel ratio (kWh/L) was calculated for various operating conditions. Furthermore, the data were used in a hypothetical duty cycle to estimate DCT and ECT availability within a work shift.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".