MétaCan
Menu
Back to cohort
Record W1966250405 · doi:10.1109/carpi.2014.7030069

An integrated approach for non-destructive testing of ACSR conductors: Early deployments of robotized sensors

2014· article· en· W1966250405 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Line Inspection Robots
Canadian institutionsHydro-Québec
FundersHydro-Québec
KeywordsElectrical conductorRoboticsNondestructive testingLine (geometry)Computer scienceArtificial intelligenceKey (lock)Lightning strikeVisual inspectionRobotPower (physics)EngineeringElectrical engineeringComputer visionComputer security

Abstract

fetched live from OpenAlex

Commonly used methods to inspect power line consist of visual camera, infrared camera, and binoculars. In the recent years, power line robotics has come to play a role for very detailed visual inspections and other tasks. These methods are adequate to detect damage caused by lightning strikes or exterior damages. Nothing is detected that the eyes cannot see. In order to probe under the surface of conductors, inspection methods with a certain degree of penetrating power are required. This paper lists some of the most relevant ACSR's ageing mechanism and proposes applicable sensor technologies to assess them. Then, since mid-term objectives include deploying these sensors via power line mobile robots such as LineScout, integration consideration are discussed. Finally, the paper presents preliminary results obtained with three different types of sensors and shows the current stage of development of NDT technologies aimed at inspecting the core of the ACSR conductors.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.268
Threshold uncertainty score0.635

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.021
GPT teacher head0.246
Teacher spread0.225 · 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

Quick stats

Citations6
Published2014
Admission routes2
Has abstractyes

Explore more

Same topicPower Line Inspection RobotsFrench-language works237,207