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Record W2104632751 · doi:10.1104/pp.114.248708

Insights into Conifer Giga-Genomes

2014· review· en· W2104632751 on OpenAlexaff
Amanda R. De La Torre, İnanç Birol, Jean Bousquet, Pär K. Ingvarsson, Stefan Jansson, Steven J.M. Jones, Christopher I. Keeling, John Mackay, Ove Nilsson, Kermit Ritland, Nathaniel R. Street, Alvin D. Yanchuk, Philipp Zerbe, Jörg Bohlmann

Bibliographic record

VenuePLANT PHYSIOLOGY · 2014
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Phylogenetic Studies
Canadian institutionsMinistry of ForestsUniversity of British ColumbiaGenome British ColumbiaUniversité Laval
Fundersnot available
KeywordsGenomeGymnospermBiologyPinaceaePinus <genus>Evolutionary biologyPlant biologyGenome sizeFlowering plantPlant evolutionBotanyGeneGenetics

Abstract

fetched live from OpenAlex

Insights from sequenced genomes of major land plant lineages have advanced research in almost every aspect of plant biology. Until recently, however, assembled genome sequences of gymnosperms have been missing from this picture. Conifers of the pine family (Pinaceae) are a group of gymnosperms that dominate large parts of the world's forests. Despite their ecological and economic importance, conifers seemed long out of reach for complete genome sequencing, due in part to their enormous genome size (20-30 Gb) and the highly repetitive nature of their genomes. Technological advances in genome sequencing and assembly enabled the recent publication of three conifer genomes: white spruce (Picea glauca), Norway spruce (Picea abies), and loblolly pine (Pinus taeda). These genome sequences revealed distinctive features compared with other plant genomes and may represent a window into the past of seed plant genomes. This Update highlights recent advances, remaining challenges, and opportunities in light of the publication of the first conifer and gymnosperm genomes.

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)0.001
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.021
GPT teacher head0.266
Teacher spread0.245 · 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
GenreReview

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

Citations212
Published2014
Admission routes1
Has abstractyes

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