Road salt impact on soil electrical conductivity across an urban landscape
Bibliographic record
Abstract
Abstract Road salt application is a necessary component of winter road maintenance but comes with an environmental cost. Salts are transported via stormwater drainage or overland and soil throughflow to surface waterbodies, where excess ions create unfavorable or even uninhabitable conditions for freshwater organisms. Soils may retain salts during the process of overland and subsurface flow, thus acting as reservoirs that slow the transport of salt into freshwaters. Understanding the capacity and consistency of anthropogenic salt storage in urban soils may allow us to discover when and where deicing salt applications are most harmful. This article investigates the degree to which soils across a heterogeneous urban landscape retain salts. We measured the electrical conductivity (EC) of soils in an urban setting. Land covers included forests, grasslands, open spaces, low- and medium-density developments and along roadsides. We found that across land-cover types, soil carbon and porosity were correlated to EC in late summer, which suggests that pore space is an important and long-lasting reservoir for salt. In addition, more developed areas, had higher mean soil EC and greater EC variability within and between sites, with 75% of overall variance occurring within individual sites. We hypothesize that this within-site heterogeneity is driven by anthropogenic modifications to salt inputs and soil characteristics. The high EC variance in highly developed urban soils is a previously undiscussed phenomenon and highlights the fine-scale complexity of heterogeneous urban landscapes and the need for high-resolution sampling to accurately characterize urban ecosystems.
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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 | 0.000 |
| 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".