THE EVOLUTION OF HIGH PRODUCTIVITY VEHICLES IN AUSTRALIA AND THEIR BENEFITS
Bibliographic record
Abstract
Although the Australian road freight transport industry has seen three mass limits reviews in the mid 1970s, the mid 1980s and in the late 1990s, (Hassall, 2005), there were two very significant truck configuration changes that happened in the mid 1980s and then again in the early 2000s. The first was the trials of a variant of the Canadian B-train (the B-Double) which was introduced into Australia in the mid 1980s. This ‘Australian’ B-Double could achieve payloads some 30% to 40% higher than the conventional ‘semi trailer’ articulated combination. By 2016 some 18,900 of these vehicles were operational in Australia. The second adoption of new vehicle configurations started in 1999 through the National Road Transport Commission (NRTC), who adopted, and further developed, another Canadian concept, that of “Performance Based Standards” (PBS). This effectively allowed for new, flexible truck designs, as long as the vehicles performed against a set of 17 specific technical engineering performance criteria. This Performance Based Standards approach, since 1998, also allowed even larger configurations to B-Doubles to be used by operators. The benefits already of these new configurations has delivered billions of dollars in kilometre savings to the road freight industry and to its customers, as well as very significant safety benefits to the community.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".