IMPACT OF THE SEWAGE DISCHARGE FROM HOT SPRINGS TO WATER SOURCES
Bibliographic record
Abstract
Hot springs have traditionally been a tourist attraction in many parts of the world, such as Japan, Canada, Taiwan among others. During the peak tourist season, it is usually discharged into streams without treatment, which can affect the quality of the receiving water, causing negative impacts to the aquatic ecosystems. Downstream ecological impacts of several major spa recreational sites in different parts of the world have been studied, and it has been found that wastewater discharges of hot springs have adverse ecological effects. The mineral composition in hot springs, derived mainly from groundwater, is usually greater than that of stream water. Studies carried out with models such as QUAL2K (or Q2K), the modernized version of the QUAL2E (or Q2E), simulate the effect of the hot spring discharges on surface water sources, mainly negative in nature. Despite the negative consequences of the impacts on ecosystems derived from hot springs’ wastewater, it is interesting to note that there are regulations for wastewater discharges – including backwash water from swimming pools – into rivers and sewers (e.g., Germany and Canada), but not specifically for hot springs discharge. Nonetheless, this evidence indicates a necessity for the authorities to increase the control of the use of hot springs and the discharge of their untreated waters. In Colombia, Resolution 631 of 2015 regulates the discharge of wastewater into rivers and sewers. Yet, it does not consider parameters for the discharge of hot springs. However, the authors deem it necessary to advance in the investigation on the contamination of the wastewaters coming from hot springs, and think about a sustainable tourism of hot springs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.005 | 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 teacher head, 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".