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Record W2050161820 · doi:10.1186/1753-6561-5-s7-p79

Identification of genes that contribute to drought stress tolerance in Populus

2011· article· en· W2050161820 on OpenAlexafffund
Muhammad Arshad, Kamal Kanti Biswas, Jim Mattsson, Sherryl R. Bisgrove, Áine L. Plant

Bibliographic record

VenueBMC Proceedings · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBioenergy crop production and management
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaHigher Education Commision, Pakistan
KeywordsIdentification (biology)Drought stressMedicineDrought toleranceGeneComputational biologyStress (linguistics)BioinformaticsBiologyBotanyGenetics

Abstract

fetched live from OpenAlex

Background The cultivation of poplars (Populus spp.) is favored for forestry and reclamation purposes all over the northern hemisphere where they represent a commercially important resource. Poplars may become a component of programs to optimize carbon sequestration however; poplars are generally regarded as drought sensitive. The patterns of episodic drought over the last decade suggest that the development of drought tolerant poplar genotypes could be a useful tool to achieve sustained forest productivity [1]. Previous reports have shown that expression of hundreds of poplar genes changes in response to drought, presenting a problem in the identification of genes that are more important than others in counteracting the harmful effects of drought [2,3]. The genus Populus contains many fast growing hybrids that show varied drought tolerance according to genotype [4]. Hence, there is genetic variation among poplar hybrids that can be used to identify genes that contribute to drought stress tolerance. Despite extensive physiological and morphological descriptions of the response of Populus to drought, little work has been undertaken to explain genotype differences at the gene level. Therefore, this research has been undertaken and its major objective is to identify the genes that contribute to drought stress tolerance in poplar by correlating the physiological responses to gene expression. These genes may potentially be used as molecular markers in the drought tolerance breeding programs.

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 score0.293
Threshold uncertainty score0.123

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 head0.040
GPT teacher head0.226
Teacher spread0.186 · 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

Citations1
Published2011
Admission routes2
Has abstractyes

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