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Record W2020595403 · doi:10.1134/s0020168507090087

Growth of corrosion-resistant manganese oxide coatings on an aluminum alloy

2007· article· en· W2020595403 on OpenAlex
Sergei A. Kulinich, M. Farzaneh, Xi‐Wen Du

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.

Bibliographic record

VenueInorganic Materials · 2007
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsCorrosionMaterials scienceAlloyChromate conversion coatingNucleationMetallurgyOxideConversion coatingManganeseCoatingAluminium6111 aluminium alloyComposite materialChemistry

Abstract

fetched live from OpenAlex

Data are presented on the nucleation and growth of corrosion-resistant manganese-oxide-based films on the surface of aluminum alloy 2024 in an alkaline KMnO4 solution at room temperature and elevated temperatures, which accelerate film growth. We consider the morphological evolution of the films and secondphase particles present on the alloy surface, which impair the corrosion resistance of the alloy. Also addressed are the feasibility of MnO 4 − incorporation into the growing film and the associated ability of the coating to selfheal when slightly damaged. Such coatings are a viable alternative to chromate-based coatings, which are currently in wide use.

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 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 score0.005
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.016
GPT teacher head0.262
Teacher spread0.246 · 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