Log Truck Transportation on Public Roads in New Zealand
Bibliographic record
Abstract
Industrial forestry within the Southland region of New Zealand is forecast to double over the next 20 years as the availability of wood from production forest plantations increases dramatically. This is generating unprecedented pressure on the public road network. An issue has arisen between government and industry over which should pay for the increasing road upgrading, rehabilitation, and maintenance costs generated by the burgeoning traffic, with the Southland Regional Council proposing a fourfold increase in the road-related rates (tax) charged to the forest industry. In an effort to produce an independent model of the forest industry's use of the road system, regional spatial data sets characterizing the wood supply, demand for wood, and road network were assembled and verified. The transportation geographic information system software package TransCAD was then used to determine the optimum distribution pattern for Southland's wood production. It was confirmed that an accurate model of the current transportation of wood in the region had been created. Distribution patterns under four input scenarios were analyzed. For domestic sawlogs, rearranging supply contracts was predicted to provide transportation savings, measured in annual tonne-kilometers, of just under 40%. On the other hand, there appeared to be no gains to be made in using unconstrained haul routes—the routes in current use are near the optimum. The predictive power of the model was illustrated for the case of chip logs, in which significant changes in traffic patterns were predicted for the 2009 harvest year, along with an order-of-magnitude increase in traffic volumes. The work revealed that the origin of the fourfold rate increase to be charged to forestry is in an arbitrary “perceived damage factor” applied to the numerically modeled traffic flows.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".