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Record W2015849735 · doi:10.5566/ias.v27.p1-10

NUCLEI SHAPE ANALYSIS, A STATISTICAL APPROACH

2011· article· en· W2015849735 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

VenueImage Analysis & Stereology · 2011
Typearticle
Languageen
FieldMathematics
TopicMorphological variations and asymmetry
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
Fundersnot available
KeywordsPrincipal component analysisCharacterization (materials science)Type (biology)Focus (optics)Principal (computer security)MathematicsComputer scienceArtificial intelligenceBiological systemPattern recognition (psychology)PhysicsGeologyBiologyOptics

Abstract

fetched live from OpenAlex

The method presented in our paper suggests the use of Functional Data Analysis (FDA) techniques in an attempt to characterise the nuclei of two types of cells: Cancer and non-cancer, based on their 2 dimensional profiles. The characteristics of the profile itself, as traced by its X and Y coordinates, their first and second derivatives, their variability and use in characterization are the main focus of this approach which is not constrained to star shaped nuclei. Findings: Principal components created from the coordinates relate to shape with significant differences between nuclei type. Characterisations for each type of profile were found.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.620
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0180.000

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 head0.068
GPT teacher head0.314
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