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Record W4386870722 · doi:10.1007/978-3-031-43980-3_8

Data Structures for SMEM-Finding in the PBWT

2023· article· en· W4386870722 on OpenAlexafffund
Paola Bonizzoni, Christina Boucher, Davide Cozzi, Travis Gagie, Dominik Köppl, Massimiliano Rossi

Bibliographic record

VenueLecture notes in computer science · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Mapping and Diversity in Plants and Animals
Canadian institutionsDalhousie University
FundersNational Human Genome Research InstituteJapan Society for the Promotion of ScienceNatural Sciences and Engineering Research Council of CanadaIsrael Institute for Biological ResearchNational Institutes of HealthNational Science Foundation
KeywordsComputer scienceDivergence (linguistics)Data structureSet (abstract data type)Encoding (memory)ComputationAlgorithmData setTheoretical computer scienceAuxiliary memoryData miningArtificial intelligence

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.003
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.049
Threshold uncertainty score0.164

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0030.006
Open science0.0040.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0490.023

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 head0.054
GPT teacher head0.307
Teacher spread0.252 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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
Published2023
Admission routes2
Has abstractno

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