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Record W4411618838 · doi:10.3390/agronomy15071534

Multiple Signals Can Be Integrated into Pathways of Blue-Light-Mediated Floral Transition: Possible Explanations on Diverse Flowering Responses to Blue Light Manipulation

2025· article· en· W4411618838 on OpenAlexaff
Yun Kong, Youbin Zheng

Bibliographic record

VenueAgronomy · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLight effects on plants
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsBlue lightTransition (genetics)BiologyBotanyEvolutionary biologyCommunicationPsychologyPhysicsGeneticsOpticsGene

Abstract

fetched live from OpenAlex

Blue light (BL) plays a crucial role in regulating floral transition and can be precisely manipulated in controlled-environment agriculture (CEA). However, previous studies on BL-mediated flowering in CEA have produced conflicting results, likely due to species-specific responses and variations in experimental conditions (such as light spectrum and intensity) as summarized in our recent systematic review. This speculation still lacks a mechanistic explanation at the molecular level. By synthesizing recent advances in our understanding of the signaling mechanisms underlying floral transition, this review highlights how both internal signals (e.g., hormones, carbohydrates, and developmental stage) and external cues (e.g., light spectrum, temperature, nutrients, stress, and magnetic fields) are integrated into the flowering pathway mediated by BL. Key signal integration nodes have been identified, ranging from photoreceptors (e.g., cryptochromes) to downstream components such as transcription factors and central flowering regulator, FLOWERING LOCUS T (FT). This signal integration offers a potential mechanistic explanation for the previously inconsistent findings, which may arise from interspecies differences in photoreceptor composition and variation in the expression of downstream components influenced by hormonal crosstalk, environmental conditions, and developmental stage, depending on the specific context. This review provides novel molecular insights into how BL modulates floral transition through interactions with other signals. By systematically compiling and critically assessing recent research findings, we identify key research gaps and outline future directions, particularly the need for more studies in agriculturally important crops. Furthermore, this review proposes a conceptual framework for optimizing BL-based lighting strategies and exploring underexamined interaction factors in the regulation of flowering.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.232
Teacher spread0.205 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2025
Admission routes1
Has abstractyes

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