Bibliographic record
Abstract
This article reports on how the City of Toronto updated its salt management practices to keep the use of salt to a minimum and reduce costs and damage to the environment from its applications. Among the steps taken were: converting about 45% of the 200 deicing trucks so that the could use liquid anti-icing and pre-wetting products; create new training programs and present them to staff; upgrade salt storage facilities; acquired more snow melters to reduce the need for snow dumps; and establish salt working groups in Toronto and partnering with groups in other cities. New record-keeping systems were created to make it easier to monitor salt use by route and by vehicle. The city now requires electronic dispensing controls on all salt contractors' trucks and most of the city's in-house fleet so that the rate of salt flow and spinner speed can be regulated to make sure most of it stays on the road, not on the shoulder. The city also mixes sand with salt when conditions permit and pre-wets the road salt to cut down on over-spray and speed salt's effectiveness. Snow dumping sites were also re-organized and engineered in order to ensure run-off.
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 | 0.000 | 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) | 0.004 | 0.002 |
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; both teacher heads agree on what is shown here.
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".