Assessing Hydrology, Biogeochemistry, and Organic Micropollutants in an Urban Stream‐Aquifer System: An Interdisciplinary Data Set
Bibliographic record
Abstract
Abstract Urban expansion leads to increasing water pollution, impacting both human health and ecosystems. This decline in water quality often stems from insufficient wastewater treatment, along with runoff from both urban and agricultural areas. Water quality degradation challenges our efforts for sustainable water management and hinders progress toward the UN's Sustainable Development Goals (SDGs), particularly SDG6. Within aquatic environments, the hyporheic zone—the subsurface area where surface water and groundwater mix—plays a crucial role in facilitating pollutant turnover and overall aquatic health. Mixing between surface water and groundwater generates diverse microhabitats in the streambed with varying levels of oxygen, temperature, and chemical composition, which in turn allows diverse microbial communities to strive. The complexity of exchange flows within the hyporheic zone and associated turnover processes of pollutants and nutrients can only be assessed with detailed, cross‐disciplinary data sets including data about hydrology, climatology, biogeochemistry, and the subsurface composition. However, integrated data sets of this kind are seldom available. As a result, the drivers behind pollutant dynamics in stream‐aquifer systems are still not fully understood. Addressing this knowledge deficit, we present a comprehensive unique data set from an urban stream‐aquifer system in Switzerland spanning over 6 months. Incorporating hydrometric, tracer, nutrient, microbial and organic micropollutant data, our data set can help to shed light on the intricate mechanisms governing hyporheic exchange flows, as well as nutrient and organic micropollutant cycling in urban environments.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".