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Record W3108256329 · doi:10.31399/asm.hb.v13b.a0003807

Corrosion of Weathering Steels

2005· book-chapter· en· W3108256329 on OpenAlexaff

Bibliographic record

VenueASM International eBooks · 2005
Typebook-chapter
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsMitel (Canada)
Fundersnot available
KeywordsWeatheringWeathering steelCorrosionMetallurgyMaterials scienceGeologyGeochemistry

Abstract

fetched live from OpenAlex

Abstract Weathering steels contain deliberate additions of alloying elements intended to increase the atmospheric corrosion resistance of steel. This article provides an overview of atmospheric corrosion testing. It describes the estimation of the atmospheric corrosion behavior of weathering steels by two methods such as short-term exposure tests and calculation of a corrosion index based on the steel composition. The article highlights some generalities about corrosion mechanisms. Based on the mechanism of atmospheric corrosion resistance of weathering steel, working rules for creating the protective oxide film have evolved. The article also provides case histories that illustrate both the violations of these rules and suggestions on how to avoid certain maintenance problems that may be encountered with weathering steels.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.022
GPT teacher head0.254
Teacher spread0.232 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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
Published2005
Admission routes1
Has abstractyes

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