Review of different types of mountain springs and mineral waters from Bulgaria based on their natural origin and health benefits
Bibliographic record
Abstract
Mineral water has been renowned for its health benefits for over two millennia, with historical records indicating its use for rapid recovery and wound healing, especially among soldiers engaged in military activities. Over time, baths with mineral water became popular for therapeutic purposes. In the 19th century, analyses revealed that regions with mountains boasted many long-lived individuals and centenarians, with factors such as pure mountain water, fresh air, and quality food potentially contributing to longevity. This publication comprehensively reviews selected natural waters, including mineral and mountain waters. The author conducted extensive studies in Bulgaria from 2012 to 2019 involving 477 long-lived individuals and their brothers and sisters living in mountain and field areas. The investigation assessed their heredity, body weight, health, psychological status, tobacco smoking, physical activity, food, and water consumption. Emphasizing the importance of medical prevention and care, the study sheds light on essential aspects of longevity today. One significant conclusion drawn from this research is the crucial role of balancing certain minerals in both water and food for human health and longevity. These minerals include Calcium (Ca2+), Magnesium (Mg2+), Sodium (Na+), Potassium (K+), Zinc (Zn2+), and Manganese (Mn2+). Furthermore, a detailed analysis of the physicochemical properties of water in longevity zones reveals lower calcium ions (Ca2+) levels compared to other regions, ranging from 6 to 20 mg.L-1. Notably, regions like Nova Scotia, Canada, known for supercentenarians for over 110 years, have water with such calcium levels. Similarly, longevity is observed in areas of Greece characterized by Mediterranean cuisine and mountain water. Water's physicochemical composition is significantly influenced by its natural filtration through rocks, which imbues it with beneficial minerals.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".