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Record W4317460366 · doi:10.55365/1923.x2022.20.74

Features of Providing Engineering and Infrastructure Objects with Geospatial Information

2022· article· en· W4317460366 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueReview of Economics and Finance · 2022
Typearticle
Languageen
FieldEngineering
TopicGeodetic Measurements and Engineering Structures
Canadian institutionsnot available
Fundersnot available
KeywordsGeospatial analysisGeodetic datumGeographic information systemComputer scienceRemote sensingSpatial analysisCadastreAerial surveySpatial databaseSystems engineeringConstruction engineeringData miningGeographyEngineeringCartography

Abstract

fetched live from OpenAlex

The urgency of the research is due to the fact that there is a necessity to develop and maintain new mineral deposits for which it is necessary to perform surveying and geodetic works and three-dimensional modeling of the earth's surface.Based on the obtained results the geospatial data are formed.With the help of these data we can design and equip the mineral deposits and determine the geological structures and engineering infrastructure of these deposits.In addition, the geospatial data are the basis and source information of documents during the state cadastral registration and registration of land use rights.On this base, the research has the following scientific and technical task: to analyze the possibilities of using different methods for providing GIS of engineering and infrastructure systems with the geospatial information, and with the data for 3D modeling of the studied objects.Corporate GIS is filled with the data on the state of the engineering infrastructure using the information from space surveying systems with high and medium spatial resolution, as well as survey materials from unmanned aerial vehicles and aerial laser scanning.Monitoring of engineering systems is also carried out using the data of ground geodetic surveys.The analysis of the possibility of using different methods of providing GIS geospatial information with materials from space surveys with different spatial resolution, data of unmanned aerial vehicles and laser scanning, trigonometric leveling is conducted.The results of the research showed that the data of remote sensing of the Earth with different resolutions, of unmanned aerial vehicles, make it possible to form arrays of geospatial data necessary for organizations engaged in the operation of engineering systems that provide reference data for planning.Also, they allow to obtain data with sufficient accuracy when creating high-level geodetic justification, when it is necessary to increase the accuracy to III and IV classes.It was established that instead of labor-intensive geometric leveling, it is advisable to supplement the data with information obtained by the method of trigonometric leveling using highprecision electronic tacheometers.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.489
Threshold uncertainty score0.290

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 head0.002
GPT teacher head0.144
Teacher spread0.141 · 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