MétaCan
Menu
Back to cohort
Record W6929510923 · doi:10.5061/dryad.7990

Data from: SNP discovery in black cottonwood (Populus trichocarpa) by population transcriptome resequencing

2010· dataset· en· W6929510923 on OpenAlexaff

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2010
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHemoglobin structure and function
Canadian institutionsBC Cancer AgencyUniversity of British Columbia
Fundersnot available
KeywordsReference genomeGenomeTranscriptomeDNA sequencingGeneSequence assemblySingle-nucleotide polymorphismWhole genome sequencingPopulation

Abstract

fetched live from OpenAlex

The western black cottonwood (Populus trichocarpa) was the first tree to have its genome fully sequenced and has emerged as the model species for the study of secondary growth and wood formation. It is also a good candidate species for the production of lignocellulosic biofuels. Here we present and make available to the research community the results of the sequencing of the transcriptome of developing xylem in 20 accessions with high throughput next generation sequencing technology. We found over 0.5 million putative SNPs in 26,595 genes that are expressed in developing secondary xylem. More than two thirds of all SNPs were found in annotated exons, with 18% and 14% in regions of the genome annotated as introns and intergenic, respectively, where only 3% and 4% of sequence reads mapped. This suggests that the current annotation of the poplar genome is remarkably incomplete and that there are many transcripts and novel genes waiting to be annotated. We hope that this resource will stimulate further research in expression profiling, detection of alternative splicing and adaptive evolution in poplar.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.003

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 head0.022
GPT teacher head0.277
Teacher spread0.255 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueData Archiving and Networked Services (DANS)Same topicHemoglobin structure and functionFrench-language works237,207