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Record W2122497464 · doi:10.3115/1564144.1564150

<i>Yawat</i>

2008· article· en· W2122497464 on OpenAlexaff
Ulrich Germann

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicNatural Language Processing Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceMarkup languageVisualizationSentenceWord (group theory)Focus (optics)PhraseJavaScriptAnnotationArtificial intelligenceNatural language processingInformation retrievalXMLProgramming languageWorld Wide Web

Abstract

fetched live from OpenAlex

Yawat is a tool for the visualization and manipulation of word- and phrase-level alignments of parallel text. Unlike most other tools for manual word alignment, it relies on dynamic markup to visualize alignment relations, that is, markup is shown and hidden depending on the current mouse position. This reduces the visual complexity of the visualization and allows the annotator to focus on one item at a time. For a bird's-eye view of alignment patterns within a sentence, the tool is also able to display alignments as alignment matrices. In addition, it allows for manual labeling of alignment relations with customizable tag sets. Different text colors are used to indicate which words in a given sentence pair have already been aligned, and which ones still need to be aligned. Tag sets and color schemes can easily be adapted to the needs of specific annotation projects through configuration files. The tool is implemented in JavaScript and designed to run as a web application.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.351
Threshold uncertainty score0.926

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.000
Scholarly communication0.0030.003
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.3510.177

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 head0.014
GPT teacher head0.244
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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

Citations54
Published2008
Admission routes1
Has abstractyes

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