Bibliographic record
Abstract
This paper describes the results of a field study designed to quantify the effects of various factors on the snow-melting performance of salt. Many tests were conducted in a realistic environment over two winter seasons, covering more than 70 snow events, with temperatures ranging from −14°C to 3°C and snowfalls ranging from ∼0.2 to 21.0 cm. For each snow event, salts were applied to a set of test sections with specific application rates, and time series performance; condition data such as snow coverage or bare pavement status, friction, pavement and air temperature, sky view, and humidity were collected. An exploratory data analysis was performed to identify the key factors influencing the snow-melting performance of salt, such as application rate, temperature, and snow amount. A multiple linear regression model was calibrated for the relationship between the key snow-melting performance indicators: bare pavement regain time and various influencing factors. The calibrated model was then applied to determine the minimum amount of salt required for achieving a given level of service under specific weather events. Although the research was motivated by the need to develop optimal salt application rates for parking lots and sidewalks, the results could be equally applicable for other transportation facilities after factors specific to the facility, such as traffic and dilution, are accounted for.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".