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Record W3199167679 · doi:10.1139/cjb-2021-0078

High temperatures disturb ovule development in field pea (<i>Pisum sativum</i>)

2021· article· en· W3199167679 on OpenAlexaffvenue
Evelyn E. Osorio, Arthur R. Davis, Rosalind Bueckert

Bibliographic record

VenueBotany · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Molecular Biology Research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsOvuleBiologyEmbryoBotanySativumMegasporeVascular bundleCultivarGynoeciumFertilisationCalloseField peaHorticultureEmbryogenesisCell biologyCell wallStamenPollen

Abstract

fetched live from OpenAlex

High temperatures affect reproductive growth and lead to yield loss in many crops. Field pea is heat sensitive, but little is known about the effect of high temperatures on field pea ovules. We investigated the impact of heat on ovules of flowers at various reproductive nodes of field pea using growth chambers. We exposed 6 cultivars exhibiting diverse heat tolerance to 4 days of heat (35 °C day/18 °C night) during early flowering. Post-treatment ovules and embryo sacs were assessed using clearing by light microscopy and fluorescence microscopy. Results indicated that greater ovule and embryo sac development occurred on some nodes, but poor ovule and embryo sac expansion resulted on other nodes of the same heat-treated plants. Whereas advanced ovule and embryo sac development were identified on heat-tolerant cultivars, a combination of advanced and less advanced ovule and embryo sac development occurred in intermediate and heat-sensitive cultivars. More than 90% of the affected ovules displayed embryos at various stages of development, which indicated disruption around fertilization or shortly thereafter. Callose accumulation around the vascular bundle within ovules suggested disruption of assimilate transport to the embryo sac. The contrasting pattern of ovule development at different nodes implied a conflict between early aging and maternal supply of heat-treated plants.

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 score0.778
Threshold uncertainty score0.457

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 head0.014
GPT teacher head0.231
Teacher spread0.217 · 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

Citations15
Published2021
Admission routes2
Has abstractyes

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