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Record W2092307878 · doi:10.1002/rob.20427

Field repair and construction of large hydropower equipment with a portable robot

2011· article· en· W2092307878 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

VenueJournal of Field Robotics · 2011
Typearticle
Languageen
FieldEngineering
TopicPower Line Inspection Robots
Canadian institutionsHydro-Québec
FundersHydro-Québec
KeywordsPenstockRobotHydropowerEngineeringWork (physics)Field (mathematics)Task (project management)ScheduleAutomationComputer scienceMechanical engineeringElectrical engineeringSystems engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Field repair work on large hydropower equipment is rarely automated due to the high complexity of the task. Generally, the work is done manually or the equipment is dismantled and repaired off site at greatly added cost and time. This paper surveys work carried out with the SCOMPI robot in the field on large hydropower equipment. SCOMPI is a small, portable, multiprocess, track‐based robot. This paper is the continuation of another paper in which the fundamentals of the robot technology are described in greater detail. Over the past 15 years, SCOMPIs have been extensively employed for a variety of field applications on equipment such as turbines, head gates, spillway gates, and penstocks. Initially designed to repair cavitation damage to turbines, the robots are now applied to reinforce turbines or to improve their performance in terms of efficiency. More recently, they have been used for the refurbishment of gates and for the construction of penstocks. © 2011 Wiley Periodicals, Inc.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.662
Threshold uncertainty score0.321

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 head0.010
GPT teacher head0.215
Teacher spread0.205 · 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