Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Truck platooning is an emerging technology that exploits the drag reduction experienced by bluff bodies moving together in close longitudinal proximity. The drag-reduction phenomenon is produced via two mechanisms: wake-effect drag reduction from leading vehicles, whereby a following vehicle operates in a region of lower apparent wind speed, thus reducing its drag; and base-drag reduction from following vehicles, whereby the high-pressure field forward of a closely-following vehicle will increase the base pressure of a leading vehicle, thus reducing its drag.</div><div class="htmlview paragraph">This paper presents a physics-guided empirical model for calculating the drag-reduction benefits from truck platooning. The model provides a general framework from which the drag reduction of any vehicle in a heterogeneous truck platoon can be calculated, based on its isolated-vehicle drag-coefficient performance and limited geometric considerations. The model is adapted from others that predict the influence of inter-vehicle distance for vehicle platoons, but extends the concept to account for cross winds and for lateral offsets between sequential vehicles, thus permitting its use for a range of modelling and simulation applications. Good agreement with experimental data sets from wind-tunnel and track tests is demonstrated in the paper.</div></div>
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".