Effect of road salt on soil and water properties in Halifax, Nova Scotia
Bibliographic record
Abstract
Road salt is a crucial public safety tool to protect people on winter roads, but it disperses from roads and impacts the environment.Increased salinity affects both soil and water systems with adverse effects observed on vegetation, nutrient cycling, and aquatic organisms.Nova Scotia applies the most road salt of all the Canadian provinces per unit area with a total of 230,182 tonnes.I compiled and analyzed water conductivity data for the Halifax Regional Municipality (HRM) from datasets provided by the Department of Fisheries and Oceans and Halifax Water to determine the spatial and temporal trends in conductivity.Conductivity has been increasing in most HRM lakes since at least 1980, but seasonal trends were inconsistent.To better understand salinity of soil and lakes, fieldwork was conducted at five lakes in Dartmouth, Nova Scotia that represented sites exposed to or protected from road salt.At each lake, we sampled water and soil on a side that was closer to roads/road salt application, and on another side further away.Statistical analysis showed no significant effect of proximity to road salt application on water conductivity (P = 0.834).There was a significant difference between protected and exposed sites (P = 0.0187).Soil electrical conductivity was also measured on both sides of the lakes at distances of 0, 10, and 20 m from the lake before (fall) and after (winter) road salt application.Soil conductivity was significantly higher at 0 m (compared to 10 m, P = 0.020, and compared to 20 m, P < 0.001) and before road salt application (P = 0.014).There was no significant effect of protection (P = 0.079) or proximity to road salt application (P = 0.184) on soil conductivity.Based on these results, I concluded that road salt is negatively impacting many lakes in the HRM. April 26th, 2021This research took place in K'jipuktuk on the traditional and unceded territory of the Mi'kmaq.I would like to thank my co-supervisors Dr.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".