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Record W1994305157 · doi:10.1109/tpami.2008.91

Correction to "Gaussian Process Dynamical Models for Human Motion" [Feb 08 283-298]

2008· article· en· W1994305157 on OpenAlexaff
Jack M. Wang, David J. Fleet, Aaron Hertzmann

Bibliographic record

VenueIEEE Transactions on Pattern Analysis and Machine Intelligence · 2008
Typearticle
Languageen
FieldEngineering
TopicHuman Motion and Animation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGaussian processComputer scienceArtificial intelligenceProcess (computing)Motion (physics)Computer visionGaussianPhysicsProgramming language

Abstract

fetched live from OpenAlex

In the above titled paper (ibid., vol. 30, no. 2, pp. 283-298, Feb 08), two figures were misprinted. The correct figures are presented here.

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)0.003
metaresearch head score (Gemma)0.039
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: Editorial · Consensus signal: Editorial
Teacher disagreement score0.091
Threshold uncertainty score0.305

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.039
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0040.004
Science and technology studies0.0030.002
Scholarly communication0.0040.004
Open science0.0050.003
Research integrity0.0080.011
Insufficient payload (model declined to judge)0.0910.049

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 head0.026
GPT teacher head0.275
Teacher spread0.249 · 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
GenreEditorial

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

Citations125
Published2008
Admission routes1
Has abstractyes

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