Environmental conditions affect phenolic content and antioxidant capacity of leaves and fruit in wild partridgeberry (<i>Vaccinium vitis-idaea</i>)
Bibliographic record
Abstract
Partridgeberry (Vaccinium vitis-idaea L.) is a good source of food and pharmaceutical ingredients, for which cultivation interest is increasing in North America. Nutrition-oriented breeding programs will benefit from an understanding of how the environment affects the biochemical traits of interest in wild populations. Total phenolic content (TPC) and antioxidant capacity (AC) as measured by the ability to capture free radicals were evaluated simultaneously in leaves and fruit of 56 wild populations across Newfoundland and Labrador, Canada. We tested variation in TPC and AC as a function of eight environmental factors, which showed different effects in leaves and fruit. Contrary to our expectations, TPC was not correlated with AC in either leaves or fruit, and mean TPC and AC were higher in fruit than in leaves. We propose a series of environment-based models for the selection of wild populations. Models for fruit involved ecoregion, temperature, and coastal proximity, and explained up to 51% of variation. While leaf models included surface water pH and sensitivity to acid rain, explaining up to 31%. We conclude that wild population selection in the province should target the North Shore Forest ecoregion and warm temperatures for fruit; and regions with low water alkalinity and pH > 6.6 for leaves.
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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".