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Record W184527810 · doi:10.5006/c2001-01544

The Effects of Wind on Local Atmospheric Corrosivity

2001· article· en· W184527810 on OpenAlexaff
Robert D. Klassen, P.R. Roberge

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsCorrosionEnvironmental scienceMetallurgyMaterials scienceMeteorologyPhysics

Abstract

fetched live from OpenAlex

Abstract The influence of wind effects on local atmospheric corrosivity was measured and modeled. Six sets of CLIMAT units were placed among devices that provided various degrees of wind sheltering near a deiced highway. Although each set was exposed to the same relative humidity, there was a 34-fold difference between the average mass loss of the most wind-protected and the least wind-protected sets. This is consistent with the concept that atmospheric corrosion rates depend primarily on salt aerosol deposition rates, which in turn depend on local wind velocity and turbulence patterns. Therefore barriers that reduce the wind velocity and turbulence intensity reduce aerosol deposition rates and consequently corrosivity rates. Detailed modeling of the wind flow patterns around two of the CLIMAT sets confirmed that the corrosivity differences were due to aerosol deposition effects. Terminology could be modified to better reflect the localized nature of atmospheric corrosivity. It is suggested that macrocorrosivity refer to the characterization of an area that is on the scale of kilometres such as a city or county; that microcorrosivity refer to the characterization of locations that are on the scale of meters such as different sites near a building or vehicle; and that nanocorrosivity refer to characterizing spots that are on the scale of centimetres.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.483
Threshold uncertainty score0.379

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 head0.004
GPT teacher head0.191
Teacher spread0.187 · 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

Citations5
Published2001
Admission routes1
Has abstractyes

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