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Laboratory tests results of a measuring station for plate bearing tests of road structures

2021· article· en· W3203218592 on OpenAlexaff
Ivan Shuliak, Serhii Sukhonosov, Oleksandr Chechuha

Bibliographic record

VenueAutomobile Roads and Road Construction · 2021
Typearticle
Languageen
FieldEngineering
TopicIndustrial Engineering and Technologies
Canadian institutionsTransport Canada
Fundersnot available
KeywordsTruckRange (aeronautics)Total stationMetrologyProcess (computing)EngineeringMeasuring instrumentBearing (navigation)SoftwareMarine engineeringComputer scienceAutomotive engineeringGeodesyGeologyArtificial intelligence

Abstract

fetched live from OpenAlex

An article devoted to the development of the latest equipment for testing road structures during the diagnostics of roads both in the process of repair and construction, and in the process of operation. A measuring station for stamp tests of road structures and their layers has been developed, which unites the test equipment into a single automated complex mounted on the base car. This eliminates the need for manual labor of the operator, increases productivity and accuracy of tests, provides the ability to perform multiple loads. At the same time, any truck or road vehicle can be used to create the load. A measuring unit with special software installed on it is used for registration and processing of measurement results. The results of laboratory tests of the measuring station, which were carried out in the laboratory of the department of metrological support of measurements of geometric quantities of SE «Ukrmetrteststandard» are highlighted. The obtained results indicate that the values of measuring force, absolute error and range of measurements of the station of movement of the station meet the regulatory requirements.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.002

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 head0.013
GPT teacher head0.218
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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 routes1
Has abstractyes

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