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Record W2151342864 · doi:10.1105/tpc.112.096362

Secretory Pathway Research: The More Experimental Systems the Better

2012· review· en· W2151342864 on OpenAlexaff
Jürgen Denecke, Fernando Aniento, Lorenzo Frigerio, Chris Hawes, Inhwan Hwang, Jaideep Mathur, Jean‐Marc Neuhaus, David G. Robinson

Bibliographic record

VenueThe Plant Cell · 2012
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPlant-Microbe Interactions and Immunity
Canadian institutionsUniversity of Guelph
FundersBiotechnology and Biological Sciences Research Council
KeywordsBiologyEctopic expressionComputational biologyGene expressionFunction (biology)MutantGeneArabidopsisCell biologyPlant cellSubcellular localizationRegulation of gene expressionProtoplastGenetics

Abstract

fetched live from OpenAlex

Transient gene expression, in plant protoplasts or specific plant tissues, is a key technique in plant molecular cell biology, aimed at exploring gene products and their modifications to examine functional subdomains, their interactions with other biomolecules, and their subcellular localization. Here, we highlight some of the major advantages and potential pitfalls of the most commonly used transient gene expression models and illustrate how ectopic expression and the use of dominant mutants can provide insights into protein function.

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)0.003
metaresearch head score (Gemma)0.002
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 score0.004
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0030.003
Science and technology studies0.0000.003
Scholarly communication0.0030.006
Open science0.0020.001
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0040.003

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 head0.222
GPT teacher head0.319
Teacher spread0.097 · 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

Citations43
Published2012
Admission routes1
Has abstractyes

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