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Record W191365292 · doi:10.2312/3dor.20141056

SHREC'14 Track: Shape retrieval of non-rigid 3D human models

2014· article· en· W191365292 on OpenAlexaff
David Pickup, Ralph R. Martin, Paul L. Rosin, Xianfang Sun, Zhi‐Quan Cheng, Zhouhui Lian, Masaki Aono, A. Ben Hamza, Alex Bronstein, Michael M. Bronstein, Shuhui Bu, Umberto Castellani, Shaokang Cheng, Valeria Garro, Andrea Giachetti, Afzal Godil, Junwei Han, Henry Johan, L. Lai, Binquan Li, Changhao Li, Haisheng Li, Roee Litman, Xiuli Liu, Z. Liu, Yijuan Lu, Atsushi Tatsuma, Jianbo Ye

Bibliographic record

VenueORCA Online Research @Cardiff (Cardiff University) · 2014
Typearticle
Languageen
FieldEngineering
Topic3D Shape Modeling and Analysis
Canadian institutionsConcordia University
Fundersnot available
KeywordsComputer scienceBenchmarkingVariety (cybernetics)Artificial intelligenceComputer graphicsTrack (disk drive)3d modelGraphicsComputer visionPattern recognition (psychology)Machine learningInformation retrievalComputer graphics (images)

Abstract

fetched live from OpenAlex

S.101-110

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)0.004
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0060.001
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0050.003
Science and technology studies0.0020.001
Scholarly communication0.0030.003
Open science0.0080.005
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0240.026

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 head0.047
GPT teacher head0.289
Teacher spread0.242 · 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

Citations75
Published2014
Admission routes1
Has abstractyes

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