Integrating Four Human Senses into Highway Landscape Process: A System Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it