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Record W2066861105 · doi:10.1029/2006eo080002

Real‐time seismic displays in museums appeal to the public

2006· article· en· W2066861105 on OpenAlexaff
Meagan Smith, John Taber, M. Hubenthal

Bibliographic record

VenueEos · 2006
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsVisitor patternAppealPublic interestEntertainmentPublic relationsConsumption (sociology)Citizen sciencePolitical scienceInternet privacyBusinessSociologyComputer scienceSocial scienceLaw

Abstract

fetched live from OpenAlex

Technology provides people with constant access to the latest news, weather, and entertainment. Not surprisingly the public increasingly demands that the most current information be available for immediate consumption. For museums striving to educate the public and to maintain and expand visitor interest, gone are the days of passively conveying scientific concepts through static displays. Instead, science museums must find creative ways to capture the public's interest—successful advocacy for research funding, solutions to environmental problems, even future generations' scientific innovation depend on this. To this end, the continuous collection and dissemination of real‐time science information by the scientific community offers museums an opportunity to capitalize on visitors' data addiction and increase the public's interest in, and understanding of, the Earth system.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.606
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.013
GPT teacher head0.243
Teacher spread0.231 · 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 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

Citations1
Published2006
Admission routes1
Has abstractyes

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