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Record W584803909

Dynamic Target Surveying

2005· article· en· W584803909 on OpenAlexvenueno aff
Howard James

Bibliographic record

VenueBridges Conversations in Global Politics and Public Policy · 2005
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topic3D Surveying and Cultural Heritage
Canadian institutionsnot available
Fundersnot available
KeywordsTotal stationBridge (graph theory)Deflection (physics)InstallationEngineeringSuspension (topology)Deformation monitoringComputer scienceStructural engineeringMechanical engineeringDeformation (meteorology)GeologyGeodesyMathematics
DOInot available

Abstract

fetched live from OpenAlex

This article describes how automatic surveying technology was used in two bridge construction and monitoring projects in China. The first project was the Lu Pu suspension bridge, which is the longest arch bridge in the world. Installing the final three connectors in the middle of the bridge presented a special challenge because of the swing, torsion and deformation of the iron-structured connectors. During the assembly surveying, automated laser total stations with automatic target recognition were used to obtain precise, real-time coordinates of reflector targets mounted on the connectors. The swing range between the two connecting parts of the bridge also made traditional surveying methods unworkable, so a remote method of surveying and calculating the dimensions of the linking section was developed. In the second project, automatic surveying technology is being used to make deformation measurements on the Yang Pu suspension bridge. The monitoring system consists of two sets of automated total stations, each controlled by a personal computer. Circular prisms were affixed to the bridge structure and a total of 148 cycles of measurements were recorded and are being used to measure deflection and elongation.

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 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.998

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 head0.018
GPT teacher head0.258
Teacher spread0.240 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2005
Admission routes1
Has abstractyes

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