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Record W2148572613

CLaC at ImageCLEF 2009

2009· article· en· W2148572613 on OpenAlexaff
Osama El Demerdash, Sabine Bergler, Leila Kosseim

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Image and Video Retrieval Techniques
Canadian institutionsConcordia University
Fundersnot available
KeywordsComputer scienceRelevance feedbackInformation retrievalRelevance (law)Task (project management)Image retrievalBlock (permutation group theory)Image (mathematics)Artificial intelligenceMathematics
DOInot available

Abstract

fetched live from OpenAlex

This paper describes our participation at ImageCLEF 2009. We participated in the photographic retrieval task (ImageCLEFPhoto). Our method is based on intermedia pseudo-relevance feedback. We have enhanced the pseudo-relevance feedback mecha-nism by using semantic selectional restrictions. We use Terrier for text retrieval and our own simple block-based visual retrieval engine. The results obtained at image-CLEF 2009 show that our method is robust and promising. However, there is room for improvement on the visual retrieval as well as the topics without cluster descriptions.

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)0.005
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.230
Threshold uncertainty score0.769

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0070.003
Science and technology studies0.0020.001
Scholarly communication0.0040.005
Open science0.0040.005
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.2300.222

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 head0.276
Teacher spread0.266 · 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 designBench or experimental
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

Citations2
Published2009
Admission routes1
Has abstractyes

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