Phenotypic diversity in antioxidant phytochemical composition among fruits from several genotypes of red raspberry (Rubus idaeus L.)
Bibliographic record
Abstract
The Pacific Northwest in North America, Russia, and Eastern Europe are three major regions of commercial raspberry production worldwide. In British Columbia, Canada, most raspberries are produced for machine harvesting and processing, while some are selected for the fresh market. Due to increasing public awareness of the benefits of consuming antioxidants for improving human health, breeding of functional foods based on phytochemical composition pyramided with other economically important traits in raspberry is desirable. In this study, genotypes of raspberry destined for the fresh market or processing were each investigated for ascorbic acid and anthocyanin compositions. Variations in these compositional traits were assessed along three consecutive years as well as among three sites in the Fraser Valley of British Columbia. There was a wide range of ascorbic acid contents among fruits from different genotypes with a trend among sample years that appeared to be dependent on seasonal temperatures. For two cultivars, eight different anthocyanins were identified, where the rest of the cultivars contained from four to six. Growing conditions influenced anthocyanin levels, while the profiles stayed consistent. Results from this study can aid in selections by geneticists for crosses to improve antioxidant traits through breeding of new raspberry genotypes.
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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 | 0.001 | 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".