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Record W2018655412 · doi:10.1144/sp406.17

Scalable and interactive visual computing in geosciences and reservoir engineering

2014· article· en· W2018655412 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGeological Society London Special Publications · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological Modeling and Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsComputer scienceScalabilityVisual analyticsComputer graphics (images)Data scienceVisualizationArtificial intelligenceDatabase

Abstract

fetched live from OpenAlex

Abstract Visual computing technologies enable more intuitive data modelling, visualization and analysis, facilitating these processes by real-time interactive visual interfaces. These technologies are essential for software in the oil and gas industry, allowing users to gain insights and actionable information when dealing with increasingly complex, multidisciplinary datasets and processes. In the context of the oil and gas industry, interactive visual computing should also scale well with the growing data size and other components of a data analytics pipeline. We present key problems and challenges motivating the research and development of scalable and interactive visual computing systems, followed by a classification of the most important research themes and related topics. Eleven case studies developed with the industry are also presented, highlighting the main cutting edge findings, limitations and achievements.

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.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.437
Threshold uncertainty score0.536

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.015
GPT teacher head0.234
Teacher spread0.219 · 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