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Record W4385703733 · doi:10.1111/wej.12897

Runoff water loaded with road de‐icing salts: Occurrence, environmental impact and treatment processes

2023· article· en· W4385703733 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueWater and Environment Journal · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsInstitut National de la Recherche Scientifique
FundersInstitut national de la recherche scientifique
KeywordsSurface runoffEnvironmental scienceDesalinationIcingElectrodialysisEnvironmental engineeringAquatic ecosystemSurface waterEnvironmental chemistryMeteorologyChemistry

Abstract

fetched live from OpenAlex

Abstract To ensure road safety during winter, it is necessary to use de‐icing salts. However, the detrimental effects of these salts on aquatic ecosystems, vegetation, lakes, ground and surface water, and soils have become a major concern in recent years. This review aims to discuss the adverse environmental impact of using de‐icing salts on the environment and explore current conventional methods used for managing and treating runoff water. Additionally, desalination techniques in terms of their effectiveness and energy requirements have been discussed. By comparing energy costs and considering the salinity levels of runoff water containing de‐icing salts, electrodialysis as a potential technique is introduced to be combined with conventional approaches for removing and recovering salt from runoff water loaded from road de‐icing salt.

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)0.000
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 score0.013
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.008
GPT teacher head0.203
Teacher spread0.194 · 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