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Record W4409503212 · doi:10.1155/atr/2036525

Evaluation of Smart Highway Operation and Maintenance Risk: Based on AHP‐FCE Model

2025· article· en· W4409503212 on OpenAlex

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Advanced Transportation · 2025
Typearticle
Languageen
FieldEngineering
TopicEvaluation and Optimization Models
Canadian institutionsnot available
Fundersnot available
KeywordsAnalytic hierarchy processTransport engineeringComputer scienceHighway maintenanceEngineeringReliability engineeringOperations research

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.685
Threshold uncertainty score0.367

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.274
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it