Long‐Term Trends in Chloride Concentrations in Shallow Aquifers near Chicago
Bibliographic record
Abstract
The rapid expansion of major cities throughout the world is resulting in the degradation of water quality in local aquifers. Increased use of road deicers since the middle of the 20th century in cities in the northern United States, Canada, and Europe has been linked to degraded ground water quality. In this article, Chicago, Illinois, and its outlying suburban areas are used as an example of the effects of urbanization in a historical context. A statistical study of historical water quality data was undertaken to determine how urbanization activities have affected shallow (<60 m) ground water quality. Chloride (Cl(-)) concentrations have been increasing, particularly in counties west and south of Chicago. In the majority of shallow public supply wells in the western and southern counties, Cl(-) concentrations have been increasing since the 1960s. About 43% of the wells in these counties have rate increases greater than 1 mg/L/year, and 15% have increases greater than 4 mg/L/year. Approximately 24% of the samples collected from public supply wells in the Chicago area in the 1990s had Cl(-) concentrations greater than 100 mg/L (35% in the western and southern counties); median values were less than 10 mg/L before 1960. The greater increase in Cl(-) concentrations in the outer counties is most likely due to both natural and anthropogenic factors, including the presence of more significant and shallower sand and gravel deposits, less curbing of major highways and streets, and less development in some parts of these counties.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".