Lakes at Risk of Chloride Contamination
Bibliographic record
Abstract
Lakes in the Midwest and Northeast United States are at risk of anthropogenic chloride contamination, but there is little knowledge of the prevalence and spatial distribution of freshwater salinization. Here, we use a quantile regression forest (QRF) to leverage information from 2773 lakes to predict the chloride concentration of all 49 432 lakes greater than 4 ha in a 17-state area. The QRF incorporated 22 predictor variables, which included lake morphometry characteristics, watershed land use, and distance to the nearest road and interstate. Model predictions had an r 2 of 0.94 for all chloride observations, and an r 2 of 0.86 for predictions of the median chloride concentration observed at each lake. The four predictors with the largest influence on lake chloride concentrations were low and medium intensity development in the watershed, crop density in the watershed, and distance to the nearest interstate. Almost 2000 lakes are predicted to have chloride concentrations above 50 mg L –1 and should be monitored. We encourage management and governing agencies to use lake-specific model predictions to assess salt contamination risk as well as to augment their monitoring strategies to more comprehensively protect freshwater ecosystems from salinization.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".