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Record W2279403904 · doi:10.1385/1-59259-966-4:69

Identification of Components in Disease-Resistance Signaling in <i>Arabidopsis</i> by Map-Based Cloning

2006· article· en· W2279403904 on OpenAlexaff
Yuelin Zhang, Jane Glazebrook, Xin Li

Bibliographic record

VenueHumana Press eBooks · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant-Microbe Interactions and Immunity
Canadian institutionsCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
Fundersnot available
KeywordsArabidopsisCloning (programming)GeneticsBiologyPositional cloningGeneComputational biologyGenomeArabidopsis thalianaMolecular cloningMutantComplementary DNAComputer science

Abstract

fetched live from OpenAlex

With the whole genome sequence and thousands of defined polymorphisms between ecotypes available, it has become much easier to clone a gene by position (map-based cloning) in Arabidopsis. Recent development of DNA-isolation methods in plants also dramatically facilitated large-scale processing of DNA samples. Here, we describe detailed protocols for each step on general scheme of map-based cloning, from mutagenesis to genetic analysis, from rough mapping to fine mapping, and at the end to cloning the gene. Not only can these methods be used to isolate genes that are involved in plant innate immunity, they can also be adapted for any forward genetics projects in Arabidopsis.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.609
Threshold uncertainty score0.320

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 integrity0.0000.000
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 head0.031
GPT teacher head0.226
Teacher spread0.195 · 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.

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

Citations14
Published2006
Admission routes1
Has abstractyes

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