MétaCan
Menu
Back to cohort
Record W4251818269 · doi:10.6028/jres.123.006

Software to Determine Sphere Center from Terrestrial Laser Scanner Data per ASTM Standard E3125-17

2018· article· en· W4251818269 on OpenAlexaff
Prem Rachakonda, Bala Muralikrishnan, Luc Cournoyer, Daniel Sawyer

Bibliographic record

VenueJournal of Research of the National Institute of Standards and Technology · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topic3D Surveying and Cultural Heritage
Canadian institutionsNational Research Council Canada
FundersPhysical Measurement Laboratory
KeywordsNISTPoint cloudLaser scanningSoftwareComputer scienceMetrologyPoint (geometry)Measure (data warehouse)LaserCenter (category theory)Remote sensingScannerSystem of measurementReference dataComputer graphics (images)OpticsArtificial intelligenceGeologyPhysicsData miningMathematicsGeometryOperating system

Abstract

fetched live from OpenAlex

Terrestrial laser scanners (TLSs) are instruments that can measure 3D coordinates of objects at high speed using a laser, resulting in high density 3D point cloud data. The Dimensional Metrology Group (DMG) at NIST performed research to support the development of documentary standards within ASTM E57 committee on 3D imaging systems. This led to the publication of the ASTM E3125-2017 standard on point-to-point distance performance evaluation of 3D imaging systems such as TLSs. To ensure that the data from different TLS systems are processed identically, the ASTM E3125- 2017 mandates the use of a common algorithm to determine the center of a sphere from point cloud data. This paper describes this algorithm and software code is provided as a download.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.684
Threshold uncertainty score0.635

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.099
GPT teacher head0.371
Teacher spread0.272 · 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 designNot applicable
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

Citations7
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Research of the National Institute of Standards and TechnologySame topic3D Surveying and Cultural HeritageFrench-language works237,207