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Record W2071032615 · doi:10.5006/1.3287853

Predicting Carbon Dioxide Corrosion of Bare Steel Under an Aqueous Boundary Layer

2004· article· en· W2071032615 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

VenueCORROSION · 2004
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCorrosionCarbonic acidCarbon steelMaterials scienceCarbon dioxideHydrochloric acidFerrousAqueous solutionBoundary layerDiffusionAnaerobic corrosionDissolutionMetallurgyInorganic chemistryChemistryThermodynamicsOrganic chemistry

Abstract

fetched live from OpenAlex

The corrosion of bare steel under an aqueous boundary layer with dissolved carbon dioxide (CO2) was modeled to investigate the effect of CO2. The model incorporated the coupled effect of CO2 diffusion, hydration, local ionic equilibria, ferrous carbonate (FeCO3) precipitation, and steel corrosion. The model was verified against published experimental data under both FeCO3-saturated and unsaturated boundary layers. Good agreement was shown under a variety of conditions. For saturated boundary layers, the results show that the corrosion rate in carbonic acid (H2CO3) is greater than in hydrochloric acid (HCl) for a given pH and that H2CO3 reduction is the cause for the increase of corrosion rate in H2CO3. Increasing temperature was found to increase corrosion rate substantially. This work provides further understanding of the CO2 corrosion mechanism and is a reliable, convenient, and practical tool for predicting the rate of CO2 corrosion.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.963

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 head0.024
GPT teacher head0.268
Teacher spread0.243 · 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