Presence of metals in a ferruginous hot spring in the Cundinamarca region, Colombia
Bibliographic record
Abstract
The presence of metals in hot springs has been associated with various adverse health effects. Although some elements are essential for humans, they are dangerous at high levels of exposure. There have been little studies on the presence of metals in hot springs in Colombia, therefore, laboratory tests were carried out over a period of six months (June to December, 2021), with spot samples every month in a ferruginous hot spring in the Cundinamarca region, Colombia. Tests were carried out for arsenic (As), chromium (Cr), mercury (Hg), lead (Pb), aluminum (Al), copper (Cu), iron (Fe), magnesium (Mg), manganese (Mn), nickel (Ni), zinc (Zn), strontium (Sr), and calcium (Ca) in the Laboratory of the Bogota Aqueduct and Sewer Company (in Spanish EAAB). Since there are no regulations in Colombia there is no regulation on the quality of hot springs, the analysis of results was carried out by comparing them with standards for drinking water and swimming pools from countries such as Canada, Germany, and the World Health Organization (WHO) as well as hot springs in Japan. It was observed that iron was the only metal that exceeded the regulations for drinking water and swimming pools.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".