MétaCan
Menu
Back to cohort
Record W2805327676 · doi:10.31031/oabb.2018.01.000523

String Matching in DNA Databases

2018· article· en· W2805327676 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

VenueOpen Access Biostatistics & Bioinformatics · 2018
Typearticle
Languageen
FieldComputer Science
TopicAlgorithms and Data Compression
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsDatabaseString (physics)Computer scienceMatching (statistics)String searching algorithmDNAInformation retrievalComputational biologyBiologyPattern matchingArtificial intelligenceMathematicsPhysicsGeneticsTheoretical physicsStatistics

Abstract

fetched live from OpenAlex

The recent development of next-generation sequencing has changed the way we carry out the molecular biology and genomic studies. It has allowed us to sequence a DNA (Deoxyribonucleic acid) sequence at a significantly increased base coverage, as well as at a much faster rate [1]. This facilitates building an excellent platform for a whole genome sequencing, and for a variety of sequencing-based analyses, including gene expressions, mapping DNA-protein interactions, whole-transcriptome sequencing, and RNA (Ribonucleic acid) splicing profiles. For example, the RNA-Seq protocol [2], in which processed mRNA is converted to cDNA and then sequenced, is enabling the identification of previously unknown genes and alternative splice variants. The whole-genome sequencing of tumour cells can uncover previously unidentified cancer-initiating mutations.

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 categoriesScholarly communication, Open science
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 score0.920
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0040.009
Open science0.0050.008
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 head0.075
GPT teacher head0.394
Teacher spread0.319 · 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