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Record W1935853284 · doi:10.1139/l11-117

Longevity of corrosion inhibitors and performance of liquid deicer products under field storage

2012· article· en· W1935853284 on OpenAlexvenueno aff
Xianming Shi, Laura Fay, Keith Fortune, Robert Smithlin, Matthew R. Johnson, Marijean M. Peterson, Andrew Creighton

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsnot available
FundersWashington State Department of TransportationMontana State UniversityWashington State UniversityU.S. Department of Transportation
KeywordsCorrosionLongevityField (mathematics)Forensic engineeringEnvironmental scienceMaterials scienceEngineeringComposite materialMathematicsBiology

Abstract

fetched live from OpenAlex

This work investigated the longevity of inhibitors and the performance of corrosion-inhibited deicer products under two storage conditions. Three liquid deicers (MgCl2-based FreezGard, CaCl2-based CCB, and NaCl+GLT) were selected for the field storage monitoring and the key properties tested included the chloride and inhibitor concentrations, corrosion parameters (Ecorr and PCR), pH, electrical conductivity, and performance parameters (Tc and IMC30°F). The three liquid deicers investigated did not lose their quality over the 14 months of field storage, regardless of the storage condition (mixed or non-mixed). As such, it is not necessary to implement any mixing for the liquid deicer tanks during storage. It is however essential to mix the tanks immediately prior to the use of the liquid deicers, to ensure uniform composition. This study also revealed that the investigated corrosion inhibitors did not show side benefits in suppressing effective temperature or in providing ice melting capacity of their corr...

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.391

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)0.0000.000

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 head0.168
Teacher spread0.162 · 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 teacher head, 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

Citations12
Published2012
Admission routes1
Has abstractyes

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