County Transportation Officials’ Perceptions of Timber Transportation Economic Importance, Infrastructure Impact, and Weight Limits in Georgia, USA
Bibliographic record
Abstract
Log truck gross vehicle weight (GVW) limits in the US state of Georgia are among the lowest of any timber-producing state in the US and are far below GVW limits in countries such as Australia, Canada, Finland, and Sweden. In the state of Georgia, log trucks travel on county and state roads between harvest sites and forest industry mills. Most county roads were not constructed to support heavy trucks and so log trucks may damage these roads, even at the low GVW limits allowed in Georgia. Local governments sometimes enact timber harvesting ordinances to constrain timber transportation and often oppose efforts to increase GVW limits. The purpose of this study was to document local transportation officials’ perceptions of timber harvesting and transportation and to measure their support or opposition to alternative log truck weights and configurations. A telephone survey of county transportation superintendents or their equivalent was conducted in Georgia during the summer of 2020. Forty-three county officials responded to the survey, yielding a response rate of 43%. Ninety-eight percent of respondents reported that timber harvesting was important to their local economy, and 86% agreed that local governments were cooperative with log truck owners. County officials were concerned about damage from overweight log trucks and mud on public roads. The average preferred GVW and tandem axle weight limits were approximately 10% lower than the existing limits. County officials opposed six- and seven-axle 45,359 kg (100,000 lbs) GVW configurations but did support allowing log trucks to operate on interstate highways at current state weight limits. Findings suggest that logging businesses and mills should focus on improving compliance with weight laws, improving the condition of log trucks, and maintaining or improving relationships with the public and local government officials.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".