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Record W2082311327 · doi:10.1145/1394251.1394266

Information retrieval facility symposium in Vienna

2008· article· en· W2082311327 on OpenAlexaboutno aff
John Tait

Bibliographic record

VenueACM SIGIR Forum · 2008
Typearticle
Languageen
FieldComputer Science
TopicResearch Data Management Practices
Canadian institutionsnot available
Fundersnot available
KeywordsIntellectual propertyLibrary scienceComputer scienceFocus (optics)Political scienceLaw

Abstract

fetched live from OpenAlex

On November 8-9, 2007 the new Information Retrieval Facility (www.ir-facility.org) held its first annual Symposium in Vienna. The focus of the symposium was patent retrieval, and it was attended by 125 experts in Information Retrieval, Intellectual Property Management and related areas. Delegates came from Korea, Hong Kong, Singapore, Australia, Canada, the US and 10 European Countries. In terms of organisations, delegates came from Universities, Patent Offices (EPO, WIPO) and from commercial organisations ranging from majors like Shell and GlaxoSmithKline to one and two person consultancies.

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)0.008
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.268
Threshold uncertainty score0.895

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.006
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.003
Science and technology studies0.0020.001
Scholarly communication0.0100.006
Open science0.0020.005
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.2680.303

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 head0.052
GPT teacher head0.305
Teacher spread0.253 · 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 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

Citations1
Published2008
Admission routes1
Has abstractyes

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