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Record W4391993897 · doi:10.32920/25254508

The Role of Risk Perception in Road Salt Management: A Policy Study in the Lake Simcoe Watershed

2024· preprint· en· W4391993897 on OpenAlexaffabout
Jenna Salvatore

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsUniversity of GuelphToronto Metropolitan University
Fundersnot available
KeywordsWatershedRisk perceptionPerceptionSalt lakeEnvironmental scienceWater resource managementGeographyPsychologyGeologyComputer scienceGeomorphology

Abstract

fetched live from OpenAlex

There have been significant increases in chloride concentrations in Ontario’s water sources as a result of road salt runoff from roads, walkways and other impervious surfaces, bringing negative environmental consequences. This study examined the role of risk perception in road salt management in the Lake Simcoe watershed. An online survey was given to members of the community, experts in the source water protection field and winter maintenance contractors, which attempted to assess perceptions of risk about road salt and examine the trade-off between perceptions of public safety versus environmental risks. Differing perceptions of risk were shown among the three groups, which could pose challenges for making collective decisions about road salt management and affect policy outcomes. Acknowledging these differences throughout decision-making processes can contribute to proper policy decisions that ensure the risks to public safety and the risks to the environment are both low.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.162
Threshold uncertainty score0.326

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.006
GPT teacher head0.239
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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