Ecological benefit of the road salt code of practice
Bibliographic record
Abstract
Despite an overall increase in total road salt used over the past 14 years (the data record in this manuscript), there has been a 26% reduction in the rate (normalized as tonnes of salt per cm of snow per km of road) of road salt application by the City of Toronto since that city implemented mitigations from the Road Salt Code of Practice. The ecological benefit of the reduced use of road salt was approximated by comparing the estimated 26% salt reduction to the distribution of chloride tolerances that has been recently published by the Canadian Council of Ministers of the Environment (i.e., CCME). Species sensitivity distributions predict that between 1 and 14% of taxa would benefit from a 26% reduction in chloride concentrations in surface waters. Assuming that a typical ‘healthy’ Canadian watercourse might support between 100 and 200 species of fish, invertebrates and plants, the Code of Practice might provide benefit to between 14 and 28 species. However, the net ecological benefit of implementing the Code may be undermined in rapidly urbanizing watersheds where road networks continue to expand at a rate of 3–5% per year and chloride loads to urban streams are steadily increasing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".