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Record W2815338869 · doi:10.1145/3215611.3215614

Navigating Data Over Time

2018· article· en· W2815338869 on OpenAlexaff
Nicola R. Di Matteo, James Blustein

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
TopicPersonal Information Management and User Behavior
Canadian institutionsDalhousie University
Fundersnot available
KeywordsComputer scienceDimension (graph theory)Fourth DimensionDomain (mathematical analysis)Representation (politics)Interface (matter)Data scienceHuman–computer interactionWorld Wide WebInformation retrieval

Abstract

fetched live from OpenAlex

Data in online archives and services are repositories of human knowledge and facts related to the lives of people. Information is visualized in a spatial domain representation that includes text structures, taxonomies, and images. Often interfaces do not offer appropriate tools to compare and navigate data over time, and the information appears as in a big "now", with no reference to the past and no view of the future. Adding the temporal dimension gives the possibility to see, for example, how places and different uses of artifacts changed in time. With the fourth dimension-time-interfaces becomes a 'time machine', a structure that better can represent and visualize knowledge. In this paper, after having described the concept of the navigation in time of data, we describe the Venice Time Machine, a project that aims to represent the millenarian evolution of Venice in social and urban terms implementing the navigation over time of the documents. Then, we propose and interfaces to navigate time using virtual reality, which is inspired by tools used to study trajectories of atomic particles. Finally we present Europeana, a vast archive online of the European knowledge, and an example of a time machine that uses its Application Program Interface.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.194
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0250.029

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 head0.480
GPT teacher head0.536
Teacher spread0.056 · 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; both teacher heads agree on what is shown here.

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
Published2018
Admission routes1
Has abstractyes

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