A fuzzy rule-based system for terrain classification in highway design
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
The choice of an incorrect terrain classification might lead to consequences in construction costs, design speed, or even safety. However, the current design criteria for terrain classification may be highly subjective. In Brazil, design guidelines use textual descriptors for three classes, namely level, rolling, and mountainous. This study proposes a fuzzy rule-based classifier to predict terrain classes based on average slope and slope variation. The classifier uses fuzzy logic, which can account for imprecise and vague definitions of the input variables. The classifier was built using topographic variables, i.e. slope variation and average slope, and experts’ knowledge. A survey was considered to extract experts’ opinions regarding different terrain classes. The classifier provided an accuracy of at least 75%, which suggests that the expert system captured the experts’ perceptions of the highway classes. As a result, the proposed system can assist decision-making by providing a more consistent method for terrain classification.
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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 | 0.000 |
| 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 it