Redefining Unusable Weeds to Beneficial Plants: Purslane as a Powerful Source of Omega-3 for the Future
Bibliographic record
Abstract
With global consumer demand shifting towards the consumption of healthier foods, it is crucial to discover new sources of edible plants with high nutritional value and low cost. Unique weeds such as purslane have the potential to be used as an untapped source of unconventional food with diverse nutrients and beneficial bioactive properties. Inflammation can cause oxidative stress related diseases including cardiovascular disorders, aging and cancer. One key nutrient of purslane is omega-3 with potential of inhibitory properties against inflammatory and estrogenic mediators. Purslane is known to be a rich source of a-linolenic acid, 18:3 ω-3, an essential fatty acid, carotenes, antioxidants and minerals. However, the precise mechanism of action of its individual components in disease prevention is unknown. This review provides a summary on the role of purslane bioactives, particularly omega-3 fatty acids as one of purslane’s main constituents with potential of anti-inflammatory and anti-estrogenic properties. The discovery of new sources of plants rich in omega-3 fatty acids may be a useful strategy in utilizing natural alternative sources of foods that can enhance human health and wellbeing.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".