Seasonal Variation of Phenolic Constituents and Medicinal Activities of Northern Labrador Tea,<i>Rhododendron tomentosum</i>ssp.<i>subarcticum</i>, an Inuit and Cree First Nations Traditional Medicine
Bibliographic record
Abstract
Northern Labrador tea, Rhododendron tomentosum ssp. subarcticum, is one of the most commonly used medicinal plants by Inuit and other First Nations peoples of Canada. The phenolic profile and seasonal variation of this commonly used medicinal plant remains largely unknown. To assess optimal harvesting time, R. tomentosum was collected in accordance with traditional knowledge practices bimonthly throughout the snow-free summer in Iqaluit, Nunavut. The antioxidant potency was measured in a DPPH radical scavenging assay, and the anti-inflammatory activity was determined with a TNF-α production assay. The seasonal variation of phenolic content was assessed with HPLC-DAD for fifteen of the most abundant phenolic compounds; (+)-catechin, chlorogenic acid, PARA-coumaric acid, quercetin 3-O-galactoside (hyperoside), quercetin 3-O-glucoside (isoquercitrin), quercetin 3-O-rhamnoside (quercitrin), quercetin pentoside, myricetin, quercetin, 3 procyanidins, and 3 caffeic acid derivatives. The most abundant constituent was (+)-catechin, which made up 19 % of the total weight of characterized phenolics. There was significant seasonal variation in the quantity of all fifteen constituents assessed, whereas there was no seasonal variation of their total sum. The antioxidant activity was positively correlated with phenolic content and negatively correlated with daylight hours. The anti-inflammatory activity was negatively correlated with caffeic acid derivative 1 and daylight hours. Together these results demonstrate that the timing of harvest of R. tomentosum impacts the plant's phenolic content and its antioxidant and anti-inflammatory activities.
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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".