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Record W2245832854

Identification of Salt-Vulnerable Areas: A Critical Step in Road Salt Management

2012· article· en· W2245832854 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueTransportation research circular · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsnot available
Fundersnot available
KeywordsIdentification (biology)Environmental planningBusinessWork (physics)Environmental resource managementProcess (computing)Environmental scienceHabitatTransport engineeringEngineeringComputer scienceEcology
DOInot available

Abstract

fetched live from OpenAlex

Northern communities repeatedly encounter snow and ice conditions forming hazardous environments on road networks during winter months. Millions of tons of road salts are applied in urban watersheds in North America for winter deicing operations. Although chlorides are nontoxic to humans, it has been shown to create toxic environments in aquatic habitats. Increasing numbers of agencies involved with winter road maintenance are working proactively to develop salt management plans that minimize the adverse environmental effects of deicing chemicals. However, the attention that has been given to another important aspect of developing a salt management plan, the identification of salt-vulnerable areas, seems to be lacking. Too few agencies attempt to identify areas vulnerable to road salts within their jurisdictions for better use of best management practices (BMPs). The low rate of participation of road agencies’ work on salt-vulnerable areas seems to be due to lack of clear guidelines, proper understanding of the process, and the perception that the process may require expensive and advanced data collection and analysis. Because the effectiveness of salt management practices is highly visible in salt-vulnerable areas, it is prudent to put more effort into identification of the vulnerable areas and take action to reduce the risks. This paper presents a risk-based approach for identification of salt-vulnerable areas considering salt application rates at varying land use types, transport pathways, and exposure to receptors. A fuzzy set methodology will be used to estimate the risk associated with the exposed receptors. Transport pathways include both surface and subsurface conveyance of chlorides. This paper also highlights the significance of the contribution by private contractors on salt loadings in urban areas. This risk-based approach would help provide the opportunity to prioritize implementation of management practices in the salt-vulnerable areas. The approach presented is based on research work at Highland Creek Watershed in Toronto, Canada, and Hanlon Creek Watershed in Guelph, Canada. Part of the research work done at Highland Creek and Hanlon Creek Watersheds was monitoring at different land use types by contractors with varying winter maintenance practices. Currently there are no guidelines with respect to salt application rates in parking lots, and as a result the quantity of applied salts tends to vary based on land use and contractor. For example, a contractor who is responsible for a commercial parking lot may apply more road salts then a contractor who is responsible for an industrial parking lot. The different perceived risks associated with the varying land use typically plays a major role in the amount of salt applied in an area, and this concept must be accounted for when identifying salt-vulnerable areas. In addition to the evaluation of the potential for reducing and optimizing salt application rates, other BMPs are identified and assessed. Lining the vegetated roadside ditches to minimize groundwater contamination and the use of capture and controlled release of chloride-laden snowmelt in storm water ponds to reduce chloride peaks in stream water are presented as possible BMPs.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.159
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.322
Teacher spread0.294 · 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