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Record W3043748680 · doi:10.1111/pbr.12845

Genetic variation of seedling traits responded to brassinosteroid and gibberellin inhibitors in maize (<i>Zea mays</i>) doubled haploid lines

2020· article· en· W3043748680 on OpenAlexaff
Kun Hu, Ying Xie, Chenglai Wu, Ursula K. Frei, Thomas Lübberstedt

Bibliographic record

VenuePlant Breeding · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetics and Plant Breeding
Canadian institutionsMinistry of Agriculture
FundersNational Institute of Food and Agriculture
KeywordsSeedlingBiologyGibberellinBrassinosteroidHeritabilityInbred strainZea maysGerminationGenetic variabilityGenetic variationPoaceaeAgronomyDoubled haploidyQuantitative trait locusHorticultureBotanyGenotypeGeneticsArabidopsisGene

Abstract

fetched live from OpenAlex

Abstract The purpose of this study was to examine the genotypic variation in maize doubled haploid (DH) lines response to brassinosteroid and gibberellin inhibitors. Plant responses to hormone inhibitors were determined in growth chamber experiments using germination paper for three different seedling treatments: application of propiconazole (Pcz), uniconazole (Ucz) or water (control). Mesocotyl length (ML) was more sensitive to hormone inhibitors, especially to the Ucz treatment, than other seedling traits. ML was significantly correlated with other traits in the Ucz treatment. All the seedling traits showed moderate‐to‐high broad sense heritability values, ranging from 0.39 to 0.82. The Euclidian genetic distances of inbred line pairs ranged from 1.27 to 19.94, indicating there was a high level of variability across the maize DH lines used in this study. DH lines with extreme MLs were identified, which can provide valuable breeding resources for improving abiotic stress tolerance, and for further genetic studies.

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 score0.914
Threshold uncertainty score0.354

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 head0.039
GPT teacher head0.201
Teacher spread0.162 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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