Chloride Retention and Infiltration in Upland Root Zone Soil Layer in the Urban Black Creek Watershed, Toronto, Ontario
Bibliographic record
Abstract
Winter road salt application has become necessary for road safety and our daily winter commute, however, the environmental damage caused by salting practices continues to be an area of concern. The overarching goal of the research was to see if the chloride retention soil root zone is a possible mechanism for delayed salt release, by assessing the watershed scale retention of chloride over the summer rain season using measured SC. Conducted in Black Creek, Toronto, this research utilized both field and lab sampling to gather SC data for statistical analysis about chloride retention, throughput, spatial, and temporal variability in Black Creek watershed urban upland soil root zones. Using linear regression and ANCOVA modelling, final statistical analysis results suggest a lack of chloride retention on a year-by-year basis, with chloride concentration taking 4 months to reach near baseline levels. Research identified land use type and distance to impervious surface were primary spatial variables contributing to chloride retention in urban root zone soil layer with combined model performance of R²= 0.583. However, high individual site variability made the model not applicable to sites with varying land use and topography. Concluding individual site variability as the greatest factor in chloride concentration of root zone soil layer, with recommendations for future research include increased spatial, site-based resolution.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".