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Record W1975347971 · doi:10.1002/wics.173

Statistics of shape

2011· review· en· W1975347971 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWiley Interdisciplinary Reviews Computational Statistics · 2011
Typereview
Languageen
FieldMathematics
TopicMorphological variations and asymmetry
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsLandmarkShape analysis (program analysis)Representation (politics)Computer scienceStatistical analysisStatisticsArtificial intelligenceMathematicsPattern recognition (psychology)Static analysis

Abstract

fetched live from OpenAlex

Abstract This article reviews various statistical methods that are available for the analysis of the shapes of images or objects. Statistical shape analysis is too large a topic to be reviewed in full. So, this review concentrates on shape analysis by means of landmarks, and in particular on the representation of landmark shapes on manifolds that was proposed by D. G. Kendall. The use of Kendall shape analysis on landmarks is briefly contrasted with alternative ways of representing and analyzing shapes. WIREs Comp Stat 2011 3 428–433 DOI: 10.1002/wics.173 This article is categorized under: Applications of Computational Statistics > Computational Mathematics

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.593
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.175
GPT teacher head0.421
Teacher spread0.246 · 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