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TORONTO1^ (MEINE DGS – annotiert. Öffentliches Korpus der Deutschen Gebärdensprache, mehrere Releases)

2018· dataset· en· W6945381154 on OpenAlexaboutno aff

Bibliographic record

VenueUniversität Hamburg · 2018
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant and Fungal Species Descriptions
Canadian institutionsnot available
FundersAkademie der Wissenschaften in Hamburg
KeywordsAnnotationGermanSign languageCover (algebra)Semantics (computer science)Interview

Abstract

fetched live from OpenAlex

Ein Type aus der Annotation von MEINEDGS mit allen seinen Tokens aus MEINEDGS: The Public DGS Corpus consists of more than 50 hours of video data from the DGS-Korpus project made available together with annotations for research purposes. In this project, data were collected all across Germany in the timeframe 2010-2012. The public corpus shows 330 informants in 4 different age groups (from 18 years on) from 13 different regions. All parts are conversations between two informants in German Sign Language (DGS). The majority of transcripts included cover discussions and reports on Deaf life and personal experiences, although there are examples of other tasks such as story retellings as well.<br>Transcripts are made available in iLex and ELAN format as well as in SRT subtitles format that can be imported in MaxQDA and other analysis tools, together with the mp4 video files in 360p50. Über diese versionsunabhängige DOI ist die jeweils neueste Version des Datensatzes auffindbar.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.014
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.210
Teacher spread0.201 · 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
GenreDataset

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
Published2018
Admission routes1
Has abstractyes

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