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Record W4213187073 · doi:10.1385/1-59745-131-2:3

Carrot (Daucus carota L.)

2006· book-chapter· en· W4213187073 on OpenAlexafffund
Owen Wally, Jayaraj Jayaraman, Zamir K. Punja

Bibliographic record

VenueHumana Press eBooks · 2006
Typebook-chapter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant tissue culture and regeneration
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDaucus carotaBiologyTransformation (genetics)ProtoplastSelectable markerGenetically modified cropsTransgeneAgrobacteriumPetiole (insect anatomy)Plant disease resistanceBotanyHorticultureMicrobiologyGeneGenetics

Abstract

fetched live from OpenAlex

Plants are susceptible to infection by a broad range of fungal pathogens. Many horticulturally important crop species lack adequate genetic resistance to disease. Studies on potential mechanisms of disease resistance in plants have revealed the importance of a range of pathogenesis-related (PR) proteins with antifungal activity in reducing colonization of plant tissues by pathogens. We are evaluating a range of PR-proteins, through heterologous expression in transgenic carrot tissues, for their effects on fungal disease development. The protocols for carrot transformation with a thaumatin-like protein are described. In addition, the use of herbicide resistance as a selectable marker in carrot transformation is illustrated. In this protocol, petiole segments from carrot seedlings are exposed to Agrobacterium for 10-30 min and co-cultivated for 3 d, after which herbicide selection is imposed until embryogenic calli are produced after 8-12 wk. The transfer of the embryogenic calli to hormone-free medium yields transgenic plantlets. This genetic transformation protocol has supported the generation of transgenic carrot plants with defined T-DNA inserts at the rate of between 1 and 3 Southern positive independent events out of 100.

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 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.137
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.031
GPT teacher head0.235
Teacher spread0.203 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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

Citations6
Published2006
Admission routes2
Has abstractyes

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