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Record W2226208872 · doi:10.2135/cropsci2015.01.0051

Condensed Tannin Accumulation during Seed Coat Development in Five Common Bean Genotypes

2015· article· en· W2226208872 on OpenAlexaff
Hanny T. Elsadr, M. A. Susan Marles, Gina V. Caldas, Matthew W. Blair, Kirstin E. Bett

Bibliographic record

VenueCrop Science · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant pathogens and resistance mechanisms
Canadian institutionsUniversity of GuelphUniversity of Saskatchewan
FundersCenters for Disease Control and PreventionConsortium of International Agricultural Research Centers
KeywordsPhaseolusBiologyCoatCondensed tanninGenotypeCultivarProanthocyanidinTanninHorticultureAgronomyBotanyPolyphenolAntioxidantGene

Abstract

fetched live from OpenAlex

ABSTRACT Condensed tannins (CT) are important determinants of the colorful seed coats characteristic of many dry bean ( Phaseolus vulgaris L.) market classes. These compounds are also important for plant development and human nutrition. Understanding the dynamics of CT accumulation during seed development and the genetic basis of this trait will contribute to the development of more nutritionally beneficial bean cultivars. Differences in patterns of CT accumulation were evaluated in the seed coats of five dry bean genotypes, which had contrasting final CT concentrations. Seed coats were assayed in developing pods taken from 6 d after flowering (DAF) onward to maturity. Condensed tannins were already present at 6 DAF in all genotypes, regardless of their final concentration. Concentrations leveled off and stabilized earlier in low CT genotypes than in those genotypes that ultimately had moderate to high concentrations. In contrast, genotypes that contained moderate to high final CT concentrations accumulated CT throughout seed coat development. Our results indicate that seed coat CT concentration in the seeds harvested during the period from 12 to 18 DAF can serve to accurately predict final CT ranking of seeds.

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)0.001
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 score0.796
Threshold uncertainty score0.233

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.053
GPT teacher head0.261
Teacher spread0.208 · 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

Citations11
Published2015
Admission routes1
Has abstractyes

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