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Record W3162575188 · doi:10.5185/vpoam.2021.0148

Experimental Evaluation of Deformation of Transport Infrastructures using Image-based Methods

2021· article· en· W3162575188 on OpenAlexaffabout
Vahid Abolhasannejad

Bibliographic record

VenueVideo Proceedings of Advanced Materials · 2021
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Maintenance and Monitoring
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsChinaDeformation (meteorology)Library scienceEngineering managementEngineeringPolitical scienceComputer scienceGeographyLaw

Abstract

fetched live from OpenAlex

Video Article Open Access Experimental Evaluation of Deformation of Transport Infrastructures using Image-based Methods Vahid Abolhasannejad1,*, Said Easa1, Shuai Dong2, Xiaoming Hunag3, Panjie Li4 1Department of Civil Engineering, Ryerson University, Toronto, ON, M5B 2K3 Canada 2School of Civil Engineering, Changsha University of Science and Technology, Changsha 410114, China 3School of Transportation, Southeast University, Nanjing 211189, China 4School of Civil Engineering, Zhengzhou University, Zhengzhou, 450001, China Vid. Proc. Adv. Mater., Volume 2, Article ID 2021-0148 (2021) DOI: 10.5185/vpoam.2021.0148 Publication Date (Web): 20 Jan 2021 Copyright © IAAM Abstract PDF

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.231
Threshold uncertainty score0.623

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 head0.017
GPT teacher head0.315
Teacher spread0.299 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

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