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Record W1540846082

LR-upward drawing of ordered sets

2009· article· en· W1540846082 on OpenAlexaff
Guy-Vincent Jourdan, Livaniaina Hary Rakotomalala, Nejib Zaguia

Bibliographic record

VenueMachine Graphics & Vision International Journal archive · 2009
Typearticle
Languageen
FieldComputer Science
TopicHandwritten Text Recognition Techniques
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceJavaDecompositionSet (abstract data type)Chain (unit)Order (exchange)Engineering drawingLine (geometry)Flow (mathematics)Computer graphics (images)Programming languageGeometryMathematicsEngineeringPhysics
DOInot available

Abstract

fetched live from OpenAlex

In this paper we introduce a new concept to draw an ordered set: the LR- Upward drawing is an upward drawing based on a chain decomposition of the order such that elements drawn on the same vertical line are always comparable and all other comparabilities flow from left to right. We describe a particular technique for automatic generation of an enhanced LR-Upward drawing for N-Free orders that are X-Cycle-Free. This technique first enhances the drawing locally, around a particular chain, and then expands the enhancement to the remaining part of the order. A Java-based implementation is also presented.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.862
Threshold uncertainty score0.681

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.300
Teacher spread0.290 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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
Published2009
Admission routes1
Has abstractyes

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