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Record W1985591785 · doi:10.1089/152091502321118874

Using CAVE <sup>®</sup> Technology for Functional Genomics Studies

2002· article· en· W1985591785 on OpenAlexaff
Christoph W. Sensen

Bibliographic record

VenueDiabetes Technology & Therapeutics · 2002
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBioinformatics and Genomic Networks
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsJavaCaveComputer scienceInterface (matter)WorkstationFunction (biology)Real time JavaOperating systemBiologyEcologyEvolutionary biology

Abstract

fetched live from OpenAlex

We have established the first Java 3D-enabled CAVE (CAVE automated virtual environment). The Java application programming interface allows the complete separation of the program development from the program execution, opening new application domains for the CAVE technology. Programs can be developed on any Java-enabled computer platform, including Windows, Macintosh, and Linux workstations, and executed in the CAVE without modification. The introduction of Java, one of the major programming environments for bioinformatics, into the CAVE environment allows the rapid development applications for genome research, especially for the analysis of the spatial and temporal data that are being produced by functional genomics experiments. The CAVE technology will play a major role in the modeling of biological systems that is necessary to understand how these systems are organized and how they function.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.460
Threshold uncertainty score1.000

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.050
GPT teacher head0.270
Teacher spread0.220 · 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.

Study designBench or experimental
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

Citations12
Published2002
Admission routes1
Has abstractyes

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