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Hidden Point Bar Method for High-Precision Industrial Surveys

2005· article· en· W2034829479 on OpenAlexafffundabout
W. F. Teskey, Bobby Paul, W. J. Teskey

Bibliographic record

VenueJournal of Surveying Engineering · 2005
Typearticle
Languageen
FieldEngineering
TopicAdvanced Measurement and Metrology Techniques
Canadian institutionsUniversity of Calgary
FundersNational Research Council CanadaNational Science Council
KeywordsBar (unit)Point (geometry)CalibrationField (mathematics)Orientation (vector space)Set (abstract data type)Point barComputer scienceArtificial intelligenceMathematicsGeometryPhysicsStatisticsGeology

Abstract

fetched live from OpenAlex

Precise three-dimensional positioning by the hidden point bar method is investigated. The investigation includes development of both closed form and least squares solutions, as well as calibration of the hidden point bar. The method is applied in the field at the University of Calgary Central Heating and Cooling Plant. In this application, known movements are introduced and then recovered using the hidden point bar method. The differences between known and recovered movements are 0.11mm or less. Special advantages of the hidden point bar method are that the target point does not have to be visible from the measurement instrument and the bar placed on the target can be set at any convenient orientation.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0010.002
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 head0.046
GPT teacher head0.286
Teacher spread0.239 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations2
Published2005
Admission routes3
Has abstractyes

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