Road salt intrusion dynamics in an ex-urban native wetland complex
Bibliographic record
Abstract
Inland freshwater wetlands throughout the northern U.S. and Canada are experiencing an increase in salinity due to road salt runoff during winter months. Salinization affects soil texture, contaminant transport, microbial activity, and plant growth in wetlands. Therefore, there is a pressing need to understand the dynamics of road salt intrusion in urbanized freshwater ecosystems. We used distributed high-resolution sensors to evaluate the dynamics of road salt intrusion into a wetland complex, Gensburg Markham Prairie (GMP), located in the ex-urban area outside Chicago, Illinois (USA). The in situ sensors measure electrical conductivity (EC), surface and groundwater level, precipitation, water temperature, and air temperature at 30-minute intervals. Water samples were collected monthly from 13 shallow groundwater wells and eight surface water locations and analyzed for Cl-, Mg2+, Na+, Ca2+, and K+. Two-years of continuous data show periodic spikes in EC during winter months, generally by an order-of-magnitude, due to intrusion of road salt applied on nearby roads. However, this behavior was not evident from monthly water samples, indicating that traditional water quality sampling methods likely miss such abrupt salt intrusion dynamics caused by rapid snowmelt runoff events. Higher levels of EC and Cl- occurred at the periphery of GMP near roadways, as well as in a preferential flow path to the interior of the wetland. Spectral analysis of EC time-series in ditches suggests that there is no correlation between salinity dynamics at super-annual timescales. This indicates that the salinity dynamics at GMP are event-driven, and the introduced solutes are rapidly exported from the site. This research supports development of improved de-icing strategies by local agencies and informs site-specific management of wetland ecosystems under anthropogenic stressors.
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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".