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Record W1986864438 · doi:10.1101/gr.3162505

Pooled genomic indexing of rhesus macaque

2005· article· en· W1986864438 on OpenAlexfundno aff
Aleksandar Milosavljevic, R. Alan Harris, Erica Sodergren, Andrew R. Jackson, Ken J. Kalafus, Anne V. Hodgson, Andrew Cree, Weilie Dai, Miklós Csürös, Baoli Zhu, Pieter J. de Jong, George M. Weinstock, Richard A. Gibbs

Bibliographic record

VenueGenome Research · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicChromosomal and Genetic Variations
Canadian institutionsnot available
FundersNational Human Genome Research InstituteNational Center for Research ResourcesNatural Sciences and Engineering Research Council of CanadaNational Institutes of HealthFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsBiologyRhesus macaqueGenomeGeneticsBacterial artificial chromosomePoolingDNA sequencinggenomic DNAComputational biologyWhole genome sequencingMacaqueReference genomeDNAGeneComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Pooled genomic indexing (PGI) is a method for mapping collections of bacterial artificial chromosome (BAC) clones between species by using a combination of clone pooling and DNA sequencing. PGI has been used to map a total of 3858 BAC clones covering approximately 24% of the rhesus macaque (Macaca mulatta) genome onto 4178 homologous loci in the human genome. A number of intrachromosomal rearrangements were detected by mapping multiple segments within the individual rhesus BACs onto multiple disjoined loci in the human genome. Transversal pooling designs involving shuffled BAC arrays were employed for robust mapping even with modest DNA sequence read coverage. A further innovation, short-tag pooled genomic indexing (ST-PGI), was also introduced to further improve the economy of mapping by sequencing multiple, short, mapable tags within a single sequencing reaction.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.070
GPT teacher head0.312
Teacher spread0.242 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations17
Published2005
Admission routes1
Has abstractyes

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