MétaCan
Menu
Back to cohort
Record W2444023861 · doi:10.1385/1-59259-242-2:029

Introduction to Microarray Experimentation and Analysis

2003· review· en· W2444023861 on OpenAlexaff
Peter W. Gieser, Gregory Bloom, Emmanuel Lazaridis

Bibliographic record

VenueHumana Press eBooks · 2003
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGene expression and cancer classification
Canadian institutionsImpact
Fundersnot available
KeywordsMessenger RNANorthern blotComputational biologyGene expressionBiologySample (material)MicroarrayMicroarray analysis techniquesGeneGeneticsChemistry

Abstract

fetched live from OpenAlex

Microarray experiments try to measure simultaneously the quantity of many specific messenger RNA (mRNA) sequences contained in a sample. These quantities are called gene expression. The sample mRNA can be extracted from human tissue, plant material, or even yeast. Because thousands of these sequences can be measured in a single experiment, scientists have a large window into the workings of a biological system. This is in contrast to use of more traditional approaches such as Northern blots, which limit research to one-gene-at-a-time experiments.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.973
Threshold uncertainty score0.845

Codex and Gemma teacher scores by category

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

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 head0.064
GPT teacher head0.357
Teacher spread0.294 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueHumana Press eBooksSame topicGene expression and cancer classificationFrench-language works237,207