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Record W2275723361 · doi:10.1115/1.4031817

Oxidation Behavior of Austenitic Stainless Steel 316L and 310S in Air and Supercritical Water

2016· article· en· W2275723361 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

VenueJournal of Nuclear Engineering and Radiation Science · 2016
Typearticle
Languageen
FieldEngineering
TopicSubcritical and Supercritical Water Processes
Canadian institutionsHitachi (Canada)University of Saskatchewan
FundersAcademy of FinlandNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMaterials scienceSupercritical fluidAlloyMetallurgySpinelAustenitic stainless steelChromiumMagnetiteOxideLayer (electronics)CorrosionComposite material

Abstract

fetched live from OpenAlex

In this study, we evaluated the oxidation resistance of austenitic stainless steels 316L and 310S in two different environments: air at 600°C and atmospheric pressure and supercritical water at 600°C and pressure of 25 MPa. Results indicated that both alloys showed good oxidation resistance in air by producing a protective oxide layer on their surface. In addition, alloy 310S exhibited lower weight gain during air oxidation compared to alloy 316L due to its higher content of chromium and nickel. Oxidation of alloy 310S in supercritical water was much lower than that of alloy 316L because of the formation of a protective layer of Mn2CrO4 spinel on the surface. No protective scale was formed on the surface of the alloy 316L, as magnetite (Fe3O4) and iron-chromium spinel (FeCr2O4) were the product of oxidation in supercritical water.

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 score0.419
Threshold uncertainty score0.173

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)0.0000.000

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 head0.208
Teacher spread0.202 · 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