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Record W2162090206 · doi:10.1139/b10-057

Mineral nutrient concentration influences sunflower infection by broomrape (<i>Orobanche cumana</i>)

2010· article· en· W2162090206 on OpenAlexvenueno aff
Pascal Labrousse, David Delmail, Marie‐Claire Arnaud, Patrick Thalouarn

Bibliographic record

VenueBotany · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Parasitism and Resistance
Canadian institutionsnot available
Fundersnot available
KeywordsOrobancheSunflowerBiologyHelianthus annuusParasitic plantNutrientShootAgronomyBotanyHorticultureHost (biology)

Abstract

fetched live from OpenAlex

Orobanche cumana Wallr., a root parasitic angiosperm, causes severe yield losses in Helianthus annuus L. (sunflower) in Europe. Until now, the only effective method of controlling this parasite has been the use of resistant sunflower genotypes. Broomrape resistance is, however, poorly understood even though previous studies have revealed several defence mechanisms. The study of a susceptible (2603) and a resistant (LR1) sunflower genotype in hydroponic co-culture showed that the degree of infection by broomrape is influenced by the concentration of nutrients in the growth medium. For the susceptible genotype, an increase in broomrape necrosis was observed when the nutrient concentration was increased. In the resistant genotype LR1, the rate of infection was reduced by increasing the concentration of mineral nutrients, measured as a decrease in broomrape attachments and a lack of underground stem development. When sunflowers were cultivated in full-strength medium, these findings correlated with a lower 14 C incorporation in broomrape and a change in carbon allocation to host plant organs with a reinforced “shoot apex sink strength”. Results demonstrated that in controlled conditions, the nutrient concentration directly affects sunflower resistance potential towards broomrape.

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 score0.919
Threshold uncertainty score0.304

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 head0.007
GPT teacher head0.208
Teacher spread0.201 · 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

Citations18
Published2010
Admission routes1
Has abstractyes

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