Integrating Four Human Senses into Highway Landscape Process: A System Approach
Bibliographic record
Abstract
Current highway landscape design guidelines focus only on the visual aspect of the landscape. This paper presents a comprehensive framework for highway landscape planning/design that considers all four senses (vision, sound, touch, and smell). The framework includes advanced technologies, such as electroencephalograms, electromyograms, galvanic skin response, and light detection and ranging that are used to evaluate the and physiological aspects of all users (drivers, pedestrians, and cyclists). In addition, two new elements are included in the framework: landscape consistency and the pavement as a landscape. The traditional landscape applications (structural features and transportation elements) and the emerging applications (tunnels, freeways, pedestrian paths, and cyclist paths) are described. Important landscape considerations, including sustainability, traffic safety, persons with disabilities, and education and research are discussed. The proposed framework, which reflects emerging developments in China, Europe, and other countries, should be of interest to highway practitioners involved in highway landscaping design.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".