Bioactive Molecules from Myrianthus arboreus, Acer rubrum, and Picea mariana Forest Resources
Bibliographic record
Abstract
Forest trees are the world’s most important renewable natural resources in terms of their dominance among other biomasses and the diversity of molecules that they produce. Forest tree extractives include terpenes and polyphenols, widely recognized for their biological activity. These molecules are found in forest by-products, such as bark, buds, leaves, and knots, commonly ignored in forestry decisions. The present literature review focuses on in vitro experimental bioactivity from the phytochemicals of Myrianthus arboreus, Acer rubrum, and Picea mariana forest resources and by-products with potential for further nutraceutical, cosmeceutical, and pharmaceutical development. Although these forest extracts function as antioxidants in vitro and may act on signaling pathways involved in diabetes, psoriasis, inflammation, and skin aging, much still remains to be investigated before using them as therapeutic candidates, cosmetics, or functional foods. Traditional forest management systems focused on wood must evolve towards a holistic approach, allowing the use of these extractives for developing new value-added products.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".