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Record W2424508342 · doi:10.1002/ghg.1605

Modeling of corrosion of steel tubing in CO<sub>2</sub> storage

2016· article· en· W2424508342 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

VenueGreenhouse Gases Science and Technology · 2016
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCorrosionCarbon steelMass transferParametric statisticsComputationWork (physics)ElectrochemistryPartial pressureMetallurgyMaterials scienceChemistryThermodynamicsComputer scienceElectrodeMathematicsPhysicsChromatographyAlgorithmPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract In this work, a mechanistic model was developed to predict the corrosion rate of steel tubing under carbon storage conditions. The model integrates a number of sub‐models that quantify contributions of interrelated steps to the corrosion process. The water chemistry sub‐model determines the solution pH and the concentrations of species. The electrochemical corrosion sub‐model assesses both charge‐transfer and mass‐transfer steps and their effects on corrosion. The scale formation and its role in corrosion are considered. The finite difference method is used to solve numerical equations, and a MetLab program was written for computation of the corrosion rate. The modeling results are validated by both laboratory testing and literature data. The parametric effects, including temperature, CO 2 partial pressure, solution salinity, pH, time, etc., on corrosion are predicted. The limitations of the model are also discussed. © 2016 Society of Chemical Industry and John Wiley &amp; Sons, Ltd

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)0.001
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 score0.432

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.018
GPT teacher head0.248
Teacher spread0.230 · 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