Glucosinolate Content of Camelina Genotypes as Affected by Applied Nitrogen and Sulphur
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".