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Record W2098115340 · doi:10.1126/science.1086391

Genome-Wide Insertional Mutagenesis of <i>Arabidopsis thaliana</i>

2003· article· en· W2098115340 on OpenAlexaff
José M. Alonso, Anna N. Stepanova, Thomas J. Leisse, Christopher J. Kim, Huaming Chen, Paul Shinn, Denise K. Stevenson, Justin Zimmerman, Pascual Barajas, Rosa Cheuk, Carmelita Gadrinab, Collen Heller, Albert Jeske, Eric Koesema, Cristina C. Meyers, Holly Parker, Lance Prednis, Yasser Ansari, Nathan Choy, Hashim Deen, M. Geralt, Nisha Hazari, Emily Hom, Meagan Karnes, Celene B. Mulholland, Ral Ndubaku, Ian Schmidt, Plinio Guzmán, Laura Aguilar‐Henonin, Markus Schmid, Detlef Weigel, David E. Carter, Trudy Marchand, Eddy Risseeuw, Debra Brogden, Albana Zeko, William L. Crosby, Charles C. Berry, Joseph R. Ecker

Bibliographic record

VenueScience · 2003
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant tissue culture and regeneration
Canadian institutionsPlant Biotechnology Institute
Fundersnot available
KeywordsInsertional mutagenesisArabidopsis thalianaGeneticsBiologyArabidopsisGeneGenomeMutagenesisSaturated mutagenesisDNAMutationMutant

Abstract

fetched live from OpenAlex

Over 225,000 independent Agrobacterium transferred DNA (T-DNA) insertion events in the genome of the reference plant Arabidopsis thaliana have been created that represent near saturation of the gene space. The precise locations were determined for more than 88,000 T-DNA insertions, which resulted in the identification of mutations in more than 21,700 of the approximately 29,454 predicted Arabidopsis genes. Genome-wide analysis of the distribution of integration events revealed the existence of a large integration site bias at both the chromosome and gene levels. Insertion mutations were identified in genes that are regulated in response to the plant hormone ethylene.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.011
GPT teacher head0.231
Teacher spread0.220 · 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 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

Citations5,093
Published2003
Admission routes1
Has abstractyes

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