Application of integral pumping tests to investigate the influence of a losing stream on groundwater quality
Bibliographic record
Abstract
Abstract. Losing streams that are influenced by wastewater treatment plant effluents and combined sewer overflows (CSO's) can be a source of groundwater contamination. Released micropollutants such as pharmaceuticals, endocrine disrupters and other ecotoxicologically relevant substances as well as inorganic wastewater constituents can reach the groundwater, where they may deteriorate groundwater quality. This paper presents a method to quantify exfiltration mass flow rates Mex of wastewater constituents from losing streams by the operation of integral pumping tests (IPT's) up- and downstream of a target section. Due to the large sampled water volume during IPT's the results are more reliable than those from conventional point sampling. We applied the method at a test site in Leipzig (Germany). Wastewater constituents K+ and NO3− showed Mex values of 1241 to 4315 and 749 to 924 mg m−1stream d−1, respectively, while Cl− (16.8 to 47.3 g m−1stream d−1) and SO42− (20.3 to 32.2 g m−1stream d−1) revealed the highest observed Mex values at the test site. The micropollutants caffeine and technical-nonylphenol were dominated by elimination processes in the groundwater between upstream and downstream wells. Additional concentration measurements in the stream and a connected sewer at the test site were performed to identify relevant processes that influence the concentrations at the IPT wells.
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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 | 0.001 |
| 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) | 0.001 | 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".