Effects of Seed Color and Growing Locations on Fatty Acid Content and Composition of Two Chia (<i>Salvia hispanica</i> L.) Genotypes
Bibliographic record
Abstract
Abstract The objective of this study was to investigate the effect of chia ( Salvia hispanica L.) seed coat color on oil content and fatty acid composition, as well as the effect of different growing areas on chemical variation. This study was carried out using white and black‐spotted chia seeds grown together at five locations of Ecuador. Oil content was not significantly ( P < 0.05) different for any of the comparative analyses performed between white and black‐spotted seeds at all, although significant differences in oil content among locations were detected. The seeds from the San Pablo location showed the highest oil concentration (34.5%). No significant differences among fatty acids at any of the location were detected between white and black‐spotted seeds; however, significant differences in fatty acids composition between sites were found. Overall, significant ( P < 0.05) differences in palmitic, oleic, linoleic, and α‐linolenic fatty acid compositions among oils from seeds grown in different locations were detected. In conclusion, this paper shows that the larger differences found in oil content and fatty acid composition are due to location (because of the environmental differences) rather than chia seed coat color.
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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 | 0.000 | 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".