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Record W2765892176 · doi:10.1016/j.dib.2017.10.063

Draft genome sequence of Japanese wood mouse, Apodemus speciosus

2017· article· en· W2765892176 on OpenAlexfundno aff
Masatoshi Matsunami, Daiji Endo, Naruya Saitou, Hitoshi Suzuki, Manabu Onuma

Bibliographic record

VenueData in Brief · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic diversity and population structure
Canadian institutionsnot available
FundersInstitute of GeneticsJapan Society for the Promotion of ScienceNational Institute of GeneticsResearch Organization of Information and Systems
KeywordsBiologyGenomeApodemusWhole genome sequencingInsert (composites)Evolutionary biologyReference genomeGeneticsGeneComputational biologyEcology

Abstract

fetched live from OpenAlex

) is one of the most common rodents in broad-leaf forests in the temperate zone of the Palaearctic region. Molecular studies of wood mice have critically enhanced the understanding of their evolution and ancestral biogeographic events. However, their molecular data are currently only limited to partial mitochondrial sequences and a few genes. Therefore, we sequenced the wood mouse genome to facilitate the acquisition of useful resources for inferring their molecular evolution. We sampled a wild wood mouse at Tsukuba, Japan, and sequenced its whole-genome using the Illumina Hiseq. 2000. To reduce the risk of non-randomness, three paired-end libraries (insert sizes: 150, 300, and 500 bp) and, two mate-pair reads (insert sizes: 8 and 20 kbp) were constructed. In total, we generated approximately 210 Gbp data. From these sequences, we reconstructed 336,124 scaffolds. These data will enhance our understanding of the evolution and ecological factors that affect their genetic constitution. The genome scaffolds generated are available in the National Center Biotechnology Information (NCBI) BioProject with accession number PRJDB5914.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.884
Threshold uncertainty score0.354

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 science0.0010.001
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 head0.052
GPT teacher head0.296
Teacher spread0.244 · 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 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

Citations10
Published2017
Admission routes1
Has abstractyes

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