Long-Term Climatic and Anthropogenic Impacts on Streamwater Salinity in New York State: INCA Simulations Offer Cautious Optimism
Bibliographic record
Abstract
The long-term application of road salts has led to a rise in surface water chloride (Cl – ) concentrations. While models have been used to assess the potential future impacts of continued deicing practices, prior approaches have not incorporated changes in climate that are projected to impact hydrogeology in the 21st century. We use an INtegrated CAtchment (INCA) model to simulate Cl – concentrations in the Tioughnioga River watershed. The model was run over a baseline period (1961–1990) and climate simulations from a range of GCMs run over three 30-year intervals (2010–2039; 2040–2069; 2070–2099). Model projections suggest that Cl – concentrations in the two river branches will continue to rise for several decades, before beginning to decline around 2040–2069, with all GCM scenarios indicating reductions in snowfall and associated salt applications over the 21st century. The delay in stream response is most likely attributed to climate change and continued contribution of Cl – from aquifers. By 2100, surface water Cl – concentrations will decrease to below 1960s values. Catchments dominated by urban lands will experience a decrease in average surface water Cl –, although moderate compared to more rural catchments.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".