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Record W2041253649 · doi:10.1093/jexbot/51.353.2045

Differential response of soybean (Glycine max (L.) Merr.) genotypes to lipo‐chito‐oligosaccharide Nod Bj V (C18:1 MeFuc)

2000· article· en· W2041253649 on OpenAlexafffund
Balakrishnan Prithiviraj, Alfred Souleimanov, Xiaomin Zhou, Donald L. Smith

Bibliographic record

VenueJournal of Experimental Botany · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLegume Nitrogen Fixing Symbiosis
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGlycineNodGenotypeOligosaccharideChemistryBiologyMolecular biologyBiochemistryGene

Abstract

fetched live from OpenAlex

Lipo-chito-oligosaccharides (LCOs) are bacteria-to-plant signal molecules essential for the establishment of rhizobia-legume symbioses. LCOs invoke a number of physiological changes in the host plants, such as root hair deformation, cortical cell division and ontogeny of complete nodule structures. The responses of five soybean cultivars to Nod BJ: V (C(18:1) MeFuc) isolated from Bradyrhizobium japonicum strain 532C were studied with a new technique. Two distinct types of root hair deformation were evident (i) bulging, in which root hairs were swollen at the tip or at the base depending on the cultivars and (ii) curling. The nodulating capacity of B. japonicum 532C varied among cultivars. Cultivars that produced a bulging reaction when treated with LCO had fewer nodules and the roots had low phenol contents. Cultivars that produced curling had higher numbers of nodules and the roots had higher amounts of phenol. Further, the roots of cultivars that showed root hair bulging were able to degrade LCO much faster than cultivars that manifested curling. The results of the present study establish relationships among the type of LCO-induced root hair deformation, root system LCO-degrading ability and nodulation capacity of soybean cultivars.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.993

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.000

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 head0.011
GPT teacher head0.236
Teacher spread0.224 · 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 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

Citations41
Published2000
Admission routes2
Has abstractyes

Explore more

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