Evaluation of Smart Highway Operation and Maintenance Risk: Based on AHP‐FCE Model
Bibliographic record
Abstract
The smart highway (SH) has an important role in realizing the strategy of strong transportation power, and operation and maintenance (O&M) management plays a decisive role in ensuring the safe and stable operation of the SH. This paper takes the Shandong F section SH project as an example and constructs the risk evaluation index system of SH O&M stage. Then, by empowering with the hierarchical analysis method, this paper evaluates the management risk of the O&M stage by using fuzzy comprehensive evaluation method and proposes risk response measures. The results show that the overall management of SH O&M in the Shandong F section is in the medium risk, in which the business risk and the smart management risk are large, the green risk and the natural environment risk are in the medium level, and the financial risk is small. The results can help enterprises effectively respond to the risk challenges facing the O&M process of the SH and promote the sustainable development of the SH.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".