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Record W2874330609 · doi:10.5539/jas.v10n8p174

Root-to-Shoot Communication to Modulate Source-Sink Relationship in Tomato Depends on Phytochromes

2018· article· en· W2874330609 on OpenAlexvenueno aff
Valéria Cury Galati, Reginaldo de Oliveira, Lucas Aparecido Gaion, Vanessa Cury Galati, Rogério Falleiros Carvalho

Bibliographic record

VenueJournal of Agricultural Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLight effects on plants
Canadian institutionsnot available
Fundersnot available
KeywordsPhytochromeShootSolanumBiologyDry weightSink (geography)BotanyHorticulture

Abstract

fetched live from OpenAlex

Phytochromes have been reported as strategic photoreceptors that can modulate the partition of photoassimilates between source and sink. However, so far, it is unknown whether phytochrome accumulation in the root is part of the control mechanisms of the source-sink relationship that modulates root and shoot growth. Thus, the objective of this work was to investigate phytochrome involvement in the source-sink relationship and in the vegetative and reproductive development of tomato plants (Solanum lycopersicum L. cv. Micro-Tom or MT). The experimental design was completely randomized with four treatments, provided by grafting combinations between aurea (au), which is phytochrome deficient, and the near isogenic line MT: (MT/MT, au/au, MT/au and au/MT). We observed differentiated responses for many parameters analyzed. For example, the root dry mass accumulation and stern diameter obtained by MT/MT, MT/au and au/MT grafting were 33% and 31% higher, respectively, than those obtained by au/au. In the au/MT combination, there were greater root dry mass and total dry mass accumulations. Based on the changes in vegetative and reproductive development observed from grafting combinations between MT and the mutant au, we can conclude that phytochromes function in the control of photoassimilate partitioning between roots and stems during tomato growth.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.926
Threshold uncertainty score0.325

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.021
GPT teacher head0.256
Teacher spread0.236 · 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 designObservational
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

Citations0
Published2018
Admission routes1
Has abstractyes

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