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Record W2013551302 · doi:10.1139/b03-019

Dehydration and dehiscence in siliques of <i>Brassica napus</i> and <i>Brassica rapa</i>

2003· article· en· W2013551302 on OpenAlexfundvenueno aff
Timothy M. Squires, Marco L.H. Gruwel, Rong Zhou, Shahab Sokhansanj, Suzanne R. Abrams, Adrian J. Cutler

Bibliographic record

VenueCanadian Journal of Botany · 2003
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSilicon Effects in Agriculture
Canadian institutionsnot available
FundersNational Research Council Canada
KeywordsSiliqueBrassica rapaBrassicaBiologyCanolaBotanyDehiscenceHorticultureArabidopsisMutant

Abstract

fetched live from OpenAlex

Silique dehiscence (shattering) in Brassica species has a pronounced effect on agricultural yields. Shattering is highly variable and difficult to quantify, and consequently there have been few studies that explore interspecies variation in shattering in relation to silique development. In this paper, a rapid and simple method has been developed for quantifying silique dehiscence. The variable-speed pod splitter is a mechanical device that provides a measure of the impact force required to trigger shattering of individual siliques. We have used the variable-speed pod splitter to show that siliques of Brassica rapa cv. Parkland were significantly more resistant to shattering than those of Brassica napus cv. Quantum. Siliques of both species became prone to shattering following a short period of rapid dehydration during which their water content fell from approx. 70% to approx. 10% (based on weight). Magnetic resonance imaging of individual siliques of varying ages revealed that water loss occurred from the inside the pericarp in B. napus cv. Quantum and from the outside of the pericarp in B. rapa cv. Parkland. We suggest a mechanism for how this difference in the pattern of water loss contributed to the difference in susceptibility to shatter between the two cultivars.Key words: shattering, magnetic resonance imaging, canola, valve.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.675
Threshold uncertainty score0.951

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 head0.005
GPT teacher head0.166
Teacher spread0.161 · 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

Citations26
Published2003
Admission routes2
Has abstractyes

Explore more

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