Investigation of correlation between remotely sensed impervious surfaces and chloride concentrations
Bibliographic record
Abstract
Faranak Amirsalaria, Jonathan Lia*, Xian Guana & William G. Bootyb a Department of Geography and Environment Management , University of Waterloo , Waterloo , Ontario , Canada , N2L 3G1 b Aquatic Ecosystem Management Research Division , National Water Research Institute , Burlington , Ontario , Canada , L7R 4A6 * E-mail: junli@uwaterloo.ca The main objective of this study is to verify the often assumed correlation between impervious surfaces and chlorides that result from the application of road salts, focusing on a case study in the selected six major watersheds within the Greater Toronto Area. Landsat-5 Thematic Mapper images collected in 1990, 1995, 2000, and 2005 and the unsupervised classification technique were utilized in the estimation of percentage imperviousness for each watershed. Chloride concentrations collected at water quality monitoring stations within the watersheds were then mapped against impervious surface estimates and their spatiotemporal distribution was assessed. The remotely sensed impervious surface maps and chloride maps were overlaid in a geographical information system environment for the investigation of their potential correlation. The main findings of this study indicate an average of 12.9% increase in impervious surface areas as well as a threefold increase in chloride concentrations between 1990 and 2005. Water quality monitoring stations exhibiting the highest amounts of chloride concentration correspond with the most impervious parts of the watersheds. The results also show that the increase in imperviousness does generate higher chloride concentrations. Correspondingly, the higher levels of chloride can potentially degrade the quality of surface waters. Through developing a novel integrated remote-sensing approach, the study was successful in identifying areas most vulnerable to surface water quality degradation by road salts.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".