MétaCan
Menu
Back to cohort
Record W2462893988

Tasks and achievements of the FIG Working Group on deformation measurements and analysis

2006· article· en· W2462893988 on OpenAlexaff
Adam Chrzanowski

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicHermeneutics and Narrative Identity
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsTerminologyGeodetic datumDeformation (meteorology)Task (project management)Computer scienceIdentification (biology)GeodesyArtificial intelligenceEngineeringOperations researchGeologyGeographySystems engineeringMeteorology
DOInot available

Abstract

fetched live from OpenAlex

Abstract: The FIG Working Group 6.1 (WG6.1), established in 1969, provides an international forum for the exchange of information on new developments in measurements and analysis of structural and ground deformation by organizing specialized international symposia and by creating international ad hoc committees (Task Forces) to solve special problems in the deformable world. Between 1975 and 2006, WG6.1 organized 12 symposia and two workshops. Between 1978 and 1986, an ad hoc committee (Task Force 6.1.1) on Deformation Analysis solved problems of the identification of unstable reference points and developed a Generalized Method for Geometrical Analysis of Deformation Measurements. In 2001, Task Force 6.1.2 presented a report on Terminology and Classification of Deformation Models. Currently, four additional Task Forces are working on various aspects of Interferometric Synthetic Aperture Radar (InSAR) applications in deformation determination, on the optimal use of laser scanners, on the analysis of cyclic deformations and vibrations,

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)0.035
metaresearch head score (Gemma)0.015
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: Empirical · Consensus signal: none
Teacher disagreement score0.035
Threshold uncertainty score0.185

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.015
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0060.004
Science and technology studies0.0030.002
Scholarly communication0.0050.003
Open science0.0040.008
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0240.030

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 head0.048
GPT teacher head0.226
Teacher spread0.178 · 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
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

Citations3
Published2006
Admission routes1
Has abstractyes

Explore more

Same topicHermeneutics and Narrative IdentityFrench-language works237,207