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Record W2561016633 · doi:10.1093/jxb/erw464

Hormonal regulation of reproductive growth under normal and heat-stress conditions in legume and other model crop species

2016· review· en· W2561016633 on OpenAlexaff
Jocelyn A. Ozga, Harleen Kaur, Raghavendra P. Savada, Dennis M. Reinecke

Bibliographic record

VenueJournal of Experimental Botany · 2016
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicSeed Germination and Physiology
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsLegumeBiologyHeat stressCropHormoneAgronomyBotanyEndocrinology

Abstract

fetched live from OpenAlex

Legume crops are grown throughout the world and provide an excellent food source of digestible protein and starch, as well as dietary fibre, vitamins, minerals, and flavonoids. Fruit and seeds from legumes are also an important source of vegetables for a well-balanced diet. A trend in elevated temperature as a result of climate change increases the risk of a heat stress-induced reduction in legume crop yield. High temperatures during the crop reproductive development phase are particularly detrimental to fruit/seed production because the growth and development of the reproductive tissues are sensitive to small changes in temperature. Hormones are signalling molecules that play important roles in a plant's ability to integrate different environmental inputs and modify their developmental processes to optimize growth, survival, and reproduction. This review focuses on the hormonal regulation of reproductive development and heat stress-induced alteration of this regulation during (i) pollination, (ii) early fruit set, and (iii) seed development that affects fruit/seed yield in legume and other model crops. Further understanding of hormone-regulated reproductive growth under non-stress and heat-stress conditions can aid in trait selection and the development of gene modification strategies and cultural practices to improve heat tolerance in legume crops contributing to improved food security.

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: Review · Consensus signal: Review
Teacher disagreement score0.812
Threshold uncertainty score0.217

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.046
GPT teacher head0.307
Teacher spread0.260 · 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
GenreReview

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

Citations93
Published2016
Admission routes1
Has abstractyes

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