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Full-length cDNAs: more than just reaching the ends

2001· review· en· W2113754798 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

VenuePhysiological Genomics · 2001
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Genetics and Reproduction
Canadian institutionsMcGill University
FundersNational Cancer Institute
KeywordsComplementary DNABiologycDNA libraryComputational biologyGenomeGeneticsBottleneckCoding regionDNA sequencingGeneComputer science

Abstract

fetched live from OpenAlex

The development of functional genomic resources is essential to understand and utilize information generated from genome sequencing projects. Central to the development of this technology is the creation of high-quality cDNA resources and improved technologies for analyzing coding and noncoding mRNA sequences. The isolation and mapping of cDNAs is an entrée to characterizing the information that is of significant biological relevance in the genome of an organism. However, a bottleneck is often encountered when attempting to bring to full-length (or at least full-coding) a number of incomplete cDNAs in parallel, since this involves the nonsystematic, time consuming, and labor-intensive iterative screening of a number of cDNA libraries of variable quality and/or directed strategies to process individual clones (e.g., 5' rapid amplification of cDNA ends). Here, we review the current state of the art in cDNA library generation, as well as present an analysis of the different steps involved in cDNA library generation.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.084
GPT teacher head0.332
Teacher spread0.247 · 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