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Record W4388652405 · doi:10.48550/arxiv.1702.06510

Algorithmes de classification et d'optimisation: participation du\n LIA/ADOC \\'a DEFT'14

2017· preprint· fr· W4388652405 on OpenAlexaff
Luis Adrián Cabrera-Diego, Stéphane Huet, Bassam Jabaian, Alejandro Molina-Villegas, Juan‐Manuel Torres‐Moreno, Marc El-Bèze, Barthélémy Durette

Bibliographic record

VenuearXiv (Cornell University) · 2017
Typepreprint
Languagefr
FieldComputer Science
TopicStatistical and Computational Modeling
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

This year, the DEFT campaign (D\\'efi Fouilles de Textes) incorporates a task\nwhich aims at identifying the session in which articles of previous TALN\nconferences were presented. We describe the three statistical systems developed\nat LIA/ADOC for this task. A fusion of these systems enables us to obtain\ninteresting results (micro-precision score of 0.76 measured on the test corpus)\n

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)0.009
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.021
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0030.003
Science and technology studies0.0020.002
Scholarly communication0.0060.004
Open science0.0040.003
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0080.005

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 head0.236
GPT teacher head0.258
Teacher spread0.022 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations0
Published2017
Admission routes1
Has abstractyes

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