MétaCan
Menu
Back to cohort
Record W4413012327 · doi:10.1007/s11998-025-01126-3

Toward sustainable steel bridge maintenance: anti-corrosion coating systems

2025· article· en· W4413012327 on OpenAlexafffund
Nafiseh Ebrahimi, Misagh Khanlarian, Mojtaba Momeni, Danick Gallant

Bibliographic record

VenueJournal of Coatings Technology and Research · 2025
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsAluminium Refining, Degassing and Filtering (Canada)Western UniversityNational Research Council Canada
FundersNational Research Council Canada
KeywordsMaterials scienceCorrosionCoatingBridge (graph theory)Composite materialForensic engineeringMetallurgyEngineering

Abstract

fetched live from OpenAlex

Abstract The paper examines the significance of anti-corrosion coating systems for steel bridges within transportation networks, with a particular focus on their long-term sustainability. The study assesses the long-term sustainability of four coating systems, analyzing their performance, cost, and environmental impact over a hypothetical 75-year bridge lifespan. Key considerations include the service life of each coating system, the frequency and extent of maintenance required, the emission of volatile organic compounds (VOCs), and the broader social costs, such as public inconvenience due to maintenance activities. The research presents detailed data, equations for estimating costs, and methodologies for sustainability analysis in varying corrosive environments (C2, C3, and C5). The findings underscore the necessity of selecting low-VOC materials to minimize environmental impact, while also considering the efficacy of corrosion protection and the associated social impacts. This comprehensive approach aims to guide stakeholders in selecting the most sustainable corrosion protection strategies, ensuring relevance in diverse and evolving environmental and societal contexts.

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.

How this classification was reachedexpand

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.347
Threshold uncertainty score0.556

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.026
GPT teacher head0.310
Teacher spread0.285 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Coatings Technology and ResearchSame topicConcrete Corrosion and DurabilityFrench-language works237,207