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Record W2075884603 · doi:10.1162/014892604323112220

A Scalable Peer-to-Peer System for Music Information Retrieval

2004· article· en· W2075884603 on OpenAlexaffabout
George Tzanetakis, Jun Gao, Peter Steenkiste

Bibliographic record

VenueComputer Music Journal · 2004
Typearticle
Languageen
FieldComputer Science
TopicMusic and Audio Processing
Canadian institutionsUniversity of Victoria
FundersMinistry of Food and Drug Safety
KeywordsPeer-to-peerComputer scienceScalabilityInformation retrievalPeer reviewWorld Wide WebMultimediaDatabase

Abstract

fetched live from OpenAlex

Computer Music Journal George Tzanetakis,* Jun Gao,† and Peter Steenkiste†‡ *Computer Science Department, Faculty of Engineering University of Victoria PO BOX 3055 STNCSC Victoria, British Columbia V8W3P6 Canada gtzan@cs.uvic.ca †Computer Science Department ‡Department of Electrical and Computer Engineering Carnegie Mellon University 5000 Forbes Avenue Pittsburgh, Pennsylvania 15213 USA {jungao,prs}@cs.cmu.edu A Scalable Peer-to-Peer System for Music Information Retrieval

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)0.002
metaresearch head score (Gemma)0.007
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 score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.002
Science and technology studies0.0030.001
Scholarly communication0.0020.004
Open science0.0030.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.004

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 head0.024
GPT teacher head0.239
Teacher spread0.215 · 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

Citations15
Published2004
Admission routes2
Has abstractyes

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