Evaluation of the Effectiveness of Direct Liquid Application for Reducing Chloride Inputs to Ryerson Campus and Urban Areas in Toronto
Bibliographic record
Abstract
In the winter of 2018/19, Ryerson University began a pilot project which saw the implementation of Direct Liquid Application (DLA) of road salts in select areas within its campus. This study evaluated the reductions in chloride applications that occurred due to the pilot, as well as estimated the chloride reductions that could occur if the project was expanded at Ryerson and if other organizations in Toronto were to adopt DLA. This was done through an analysis of recorded road salt application rates on Ryerson campus. The analysis revealed that the incorporation of DLA into Ryerson’s maintenance program reduced chloride inputs to Ryerson Campus. The analysis also illustrated that similar ‘savings’ could be expected if DLA were expanded to the rest of campus, Green P parking lots, GO train stations, and TTC streetcar waiting areas. Recommendations for future DLA implementation are given.
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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 | 0.003 | 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 | 0.000 |
| 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".