MétaCan
Menu
Back to cohort
Record W2121587139

York University at TREC 2009: Relevance Feedback Track

2009· article· en· W2121587139 on OpenAlexaff
Zheng Ye, Jimmy Xiangji Huang, Ben He, Hongfei Lin

Bibliographic record

VenueText REtrieval Conference · 2009
Typearticle
Languageen
FieldComputer Science
TopicInformation Retrieval and Search Behavior
Canadian institutionsYork University
Fundersnot available
KeywordsRelevance feedbackRelevance (law)Computer scienceWeightingTask (project management)Track (disk drive)Domain (mathematical analysis)Information retrievalSeries (stratigraphy)Artificial intelligenceMathematicsEngineeringImage retrieval
DOInot available

Abstract

fetched live from OpenAlex

We describe a series of experiments conducted in our participation in the Relevance Feedback Track. We evaluate two traditional weighting models (BM25 and DFR) for the phase 1 task, which are widely used in text retrieval domain. We also evaluate a statistics-based feedback model and our proposed feedback model for the phase 2 task. Currently, we are waiting for the overview paper to facilitate further analyses.

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)0.019
metaresearch head score (Gemma)0.027
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 score0.115
Threshold uncertainty score0.228

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.027
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0040.001
Scholarly communication0.0030.004
Open science0.0030.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0250.024

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 head0.033
GPT teacher head0.247
Teacher spread0.213 · 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

Citations14
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueText REtrieval ConferenceSame topicInformation Retrieval and Search BehaviorFrench-language works237,207