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Record W2476082766 · doi:10.2135/cropsci2016.01.0018

Glucosinolate Content of Camelina Genotypes as Affected by Applied Nitrogen and Sulphur

2016· article· en· W2476082766 on OpenAlexafffund
Yunfei Jiang, Jili Li, C. D. Caldwell

Bibliographic record

VenueCrop Science · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics, phytochemicals, and oxidative stress
Canadian institutionsUniversity of SaskatchewanDalhousie University
FundersGenome Atlantic
KeywordsCamelinaCamelina sativaGlucosinolateBiologyMealCropAgronomyCultivarBrassicaBrassicaceaeFood scienceHorticultureBotany

Abstract

fetched live from OpenAlex

Camelina [ Camelina sativa (L.) Crantz] is an alternative oil crop that has potential in aquaculture and food production. However, glucosinolates (GSs) in camelina seed and meal constrain their application in human and animal consumption. Glucosinolates are plant secondary metabolites commonly found in the Brassicaceae family. The aims of this study were to determine whether nitrogen (N) alone or in combination with sulphur (S) application affected GS concentration in camelina seeds, as well as whether different genotypes differed in GS concentrations. Our results showed that the application of 25 kg S ha −1 significantly increased GS content compared with 0 kg S ha −1 . Applied N rates were negatively correlated with the amounts of total and individual GSs when no S was applied. The low GS content with high N rates was probably due to a dilution effect of S content. Applied N rates did not affect the amounts of total and individual GSs when 25 kg S ha −1 was applied. Seed protein content was negatively correlated with the total GSs depending on growing season and genotype when no S was applied. The cultivar, Calena, had the highest amount of GSs among five genotypes. The results indicated that GSs in camelina can be manipulated by cultural management practices including N and S application.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.350

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 studies0.0000.001
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 head0.010
GPT teacher head0.227
Teacher spread0.218 · 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

Citations10
Published2016
Admission routes2
Has abstractyes

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