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Record W7047287328

Extracting information from very large datasets: modelling the erechtheion

2009· article· en· W7047287328 on OpenAlexvenueaboutno aff

Bibliographic record

VenueNPARC · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSuperconducting and THz Device Technology
Canadian institutionsnot available
Fundersnot available
KeywordsVisualizationData visualizationCultural heritageResolution (logic)High resolutionCreative visualization
DOInot available

Abstract

fetched live from OpenAlex

The National Research Council in Canada (NRCC) has created a framework for gleaning information from very large TLS and imagery datasets, an evolution demonstrated in the Erechtheion project. Cultural heritage has been a central factor in the NRCC research and development program in 3D technologies. Erechtheion is on the Acropolis, next to the Parthenon in Athens, Greece, whos refurbishment began in 1987. The 'Porch of the Maidens' is on the north side and consists of six draped female figures. NRCC has created algorithms for 3D-image processing, management and real-time visualization as part of Atelier3D.ca, a general framework evolved for acquisition, processing, modeling, analysis and visualization of very large 2D/3D datasets created from 3D point-clouds and imagery. Atelier 3D.ca allows view-dependent, real-time visualization of multi-resolution models. Algorithms have been created for displaying these datasets interactively at full resolution on inexpensive laptops or desktop computers.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.691
Threshold uncertainty score0.437

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 communication0.0000.001
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 head0.247
Teacher spread0.229 · 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 designTheoretical or conceptual
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
Published2009
Admission routes2
Has abstractyes

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