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

NUCLEI SHAPE ANALYSIS, A STATISTICAL APPROACH

2011· article· en· W2015849735 on OpenAlexaff
Alberto Nettel‐Aguirre

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.

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)0.009
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: Methods · Consensus signal: Methods
Teacher disagreement score0.006
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.003
Science and technology studies0.0010.002
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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

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
GenreMethods

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

Citations7
Published2011
Admission routes1
Has abstractyes

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