Genetic Diversity, Antioxidant Activities, and Anthocyanin Contents in Lingonberry
Bibliographic record
Abstract
Lingonberry (Vaccinium vitis-idaea L.) wild clones and cultivars were assessed for antioxidant activities, anthocyanin content, and for genetic variability using inter-simple sequence repeat (ISSR) markers. Four ISSR primers generated 113 polymorphic bands in 34 clones and eight cultivars. Cluster analysis by the unweighted pair-group method with arithmetic averages (UPGMA) separated the 41 genotypes into three main clusters, and identified the one remaining clone as an outlier. Within one cluster, the genotypes tended to form subclusters that were in agreement with a principal coordinate (PCO) analysis. Geographical distribution based on country of collection explained 12% of the total variation as revealed by analysis of molecular variance (AMOVA). Antioxidant activity and anthocyanin content were higher in the berries of clones belonging to V. vitis-idaea ssp. minus than those of the V. vitis-idaea ssp. vitis-idaea cultivars. The UPGMA clustering for chemical markers with 11 clones and seven cultivars identified two major clusters and one outlier. The ISSR markers and analyses of antioxidant activities and anthocyanin contents detected a sufficient degree of polymorphism to differentiate among lingonberries, making this technology valuable for germplasm management, and more efficient choices of parents in current lingonberry breeding programs.
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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 | 0.001 |
| 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) | 0.000 | 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".