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Record W2034427767 · doi:10.5006/1.3277315

Effect of Ferrous Ion Oxidation on Corrosion of Active Iron under an Aerated Solution Layer

2002· article· en· W2034427767 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 · 2002
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Toronto
KeywordsFerrousCorrosionAerationLayer (electronics)IonMetallurgyMaterials scienceInorganic chemistryChemistryComposite material

Abstract

fetched live from OpenAlex

A new model was developed to study the effect of ferrous ion oxidation on the corrosion of active iron. When ferrous hydroxide precipitate is present at the iron surface as the result of corrosion, the solution boundary layer near the iron surface is saturated with ferrous hydroxide and the pH and ferrous ion concentration are fixed within the layer. The ferrous hydroxide precipitate and that of ferric hydroxide are often porous and do not provide resistance to the transport of solution species. Since this work deals with active iron corrosion, a passive film at the iron surface is not considered. The results show that ferrous ion oxidation decreases corrosion because it decreases oxygen concentration in the boundary layer. The corrosion rate can be decreased by up to 13.9%. This value is independent of boundary layer thickness, temperature, and saturation factor of ferrous hydroxide. The results indicate that ferrous ion oxidation does have an effect on the rate of iron corrosion and that neglecting ferrous ion oxidation when determining iron corrosion results in an overestimate of the corrosion rate.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
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 communication0.0000.001
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 head0.032
GPT teacher head0.286
Teacher spread0.254 · 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