A new model for fuel consumption and route time computations – a case study in the Quebec forest industry
Why this work is in the frame
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Bibliographic record
Abstract
In forest transportation in Quebec, transportation rates are typically based on an estimated trip or travel time duration during which a truck travels loaded from an origin to a destination and then returns empty to the origin. These transportation rates implicitly consider fuel consumption for fuel surcharge costs in negotiation, but for most shippers and carriers, fuel consumption remains a rough estimate, leading to underestimation or overestimation of actual consumption. In this paper, we propose a new fuel consumption model and a more detailed trip time computation to support accurate estimations. The model takes into account road network characteristics that affect fuel consumption, such as road elevation profiles, including slopes that significantly affect fuel consumption compared to flat roads, and curves where trucks change speed, as well as intersections where trucks need to stop or slow down. The road network of the province of Quebec (Canada) is represented in a route network that integrates all the characteristics considered by the fuel consumption model. The model is validated in a case study using a timber truck equipped with GPS and information about overall fuel consumption between a set of refueling points. Utilizing the fuel consumption model and a route planner enables accurate estimation of fuel consumption and, consequently, associated greenhouse gas (GHG) emissions and travel times. A case study involving three companies is then conducted to analyze how more detailed information can inform new transportation rates.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it