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Record W2372547129

Optimal sensor placement for hydraulic structures based on Moran′s index I

2014· article· en· W2372547129 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.

Bibliographic record

VenueJournal of Hydroelectric Engineering · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced Measurement and Detection Methods
Canadian institutionsMinistry of Transportation of Ontario
Fundersnot available
KeywordsObservabilityModalRedundancy (engineering)Independence (probability theory)Fisher informationMathematical optimizationSpatial analysisSpatial correlationAlgorithmComputer scienceData miningMathematicsStatisticsApplied mathematicsMachine learning
DOInot available

Abstract

fetched live from OpenAlex

A spatial correlation-effective independence method, which uses Moran's index I to weigh the Fisher information matrix, is presented to solve information redundancy in application of the effective independence method to optimal sensor placement in hydraulic structures. This method can not merely judge the spatial correlation of mode shapes at candidate nodes, but obtain sensor placement schemes which maximize the contribution of each sensor to modal observability while taking the spatial correlation into account. It was applied to a guide wall and the obtained scheme was compared in detail with those by two existing methods. Results show that the new method can ensure reasonable spatial distribution and modal measurability and effectively avoid information redundancy in its application to optimal sensor placement in large hydraulic structures modeled with mesh refinement. Therefore, it is an ideal method for optimization of sensor placement.

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: Methods · Consensus signal: none
Teacher disagreement score0.821
Threshold uncertainty score0.804

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 head0.010
GPT teacher head0.235
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