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Record W4249559743 · doi:10.1002/9781119061199.ch15

Computer imaging

2017· other· en· W4249559743 on OpenAlexaff
Christine E. Haessig

Bibliographic record

Venuenot available
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomic variations and chromosomal abnormalities
Canadian institutionsVancouver General Hospital
FundersDivision of Computer and Network Systems
KeywordsThresholdingComputer scienceComputer visionArtificial intelligenceCytogeneticsPhotographyKaryotypeResolution (logic)Image qualityChromosomeComputer graphics (images)Image (mathematics)Pattern recognition (psychology)BiologyGenetics

Abstract

fetched live from OpenAlex

Understanding the relationship between the uses of phase contrast, Köhler illumination, chromosome length, staining, and band resolution for obtaining high quality images with computer-assisted digital photography is the topic of Chapter 15, Computer Imaging, in the AGT Cytogenetics Laboratory Manual, 4th ed. Karyotype imaging systems used by cytogenetics laboratories are designed to capture cell images that can be manipulated into a karyogram. The image can then be enhanced, cut into individual chromosomes, either automatically or with the aid of cut/paste features, and then arranged into a karyogram so that each chromosome can be structurally identified and evaluated alongside its respective homologue. This chapter uses the Leica Cytovision program to demonstrate these features, and includes instructions for FISH image thresholding and probe enhancement. The author also discusses the steps for creating macros that will reduce a series of routine, repetitive commands into a single stroke.

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)0.001
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.153
Threshold uncertainty score0.511

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1530.083

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 head0.006
GPT teacher head0.219
Teacher spread0.214 · 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
GenreOther

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

Citations0
Published2017
Admission routes1
Has abstractyes

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