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Record W2155219002 · doi:10.1093/bfgp/elp059

Reverse genetics techniques: engineering loss and gain of gene function in plants

2010· review· en· W2155219002 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBriefings in Functional Genomics · 2010
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Genetic and Mutation Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBiologyTILLINGReverse geneticsIn silicoGeneticsGenomeGeneComputational biologyDNA sequencingFunction (biology)Identification (biology)Loss functionPhenotype

Abstract

fetched live from OpenAlex

Genetic analysis represents a powerful tool that establishes a direct link between the biochemical function of a gene product and its role in vivo. Genome sequencing projects have identified large numbers of plant genes for which no role has yet been defined. To address this problem a number of techniques have been developed, over the last 15 years, to enable researchers to identify plants with mutations in genes of known sequence. These reverse genetic approaches include RNAi and related technologies and screening of populations mutagenised by insertion (PCR), deletion (PCR) and point mutation (TILLING), each with its own strengths and weaknesses. The development of next-generation sequencing techniques now allows such screening to be done by sequencing. In the future, it is likely that the genomes of thousands of plants from mutagenised populations will be sequenced allowing for the identification of plants with mutations in specific genes to be done in silico.

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.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score0.470

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.034
GPT teacher head0.232
Teacher spread0.198 · 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