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Record W3097186485 · doi:10.31399/asm.hb.v13b.a0003830

Corrosion of Zinc and Zinc Alloys

2005· book-chapter· en· W3097186485 on OpenAlexaff

Bibliographic record

VenueASM International eBooks · 2005
Typebook-chapter
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsTeck (Canada)
Fundersnot available
KeywordsZincCorrosionMetallurgyZinc alloysMaterials science

Abstract

fetched live from OpenAlex

Abstract Zinc is one of the most used metals, ranking fourth in worldwide production and consumption behind iron, aluminum, and copper. This article commences with an overview of the applications of zinc that can be divided into six categories: coatings, casting alloys, alloying element in brass and other alloys, wrought zinc alloys, zinc oxide, and zinc chemicals. It discusses the corrosion and electrochemical behavior of zinc and its alloys in various environments, particularly in atmospheres in which they are most widely used. The article tabulates the corrosion rates of zinc and zinc coatings immersed in various types of waters, in different solutions in the neutral pH range, and in soils at different geographic locations in the United States. It concludes with information on the forms of corrosion encountered in zinc coatings, including galvanic corrosion, pitting corrosion, and intergranular 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.

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: Other · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.016
GPT teacher head0.235
Teacher spread0.219 · 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
GenreOther

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

Citations26
Published2005
Admission routes1
Has abstractyes

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