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Record W2186881238 · doi:10.5006/i0010-9312-68-6-468

<i>Technical Note:</i> Examination of Focused Ion Beam-Sectioned Surface Films Formed on AM60B Mg Alloy in an Aqueous Saline Solution

2012· article· en· W2186881238 on OpenAlexfundno aff
J.R. Kish, Yifan Hu, Jianzhuo Li, Wenyue Zheng, Joseph R. McDermid

Bibliographic record

VenueCORROSION · 2012
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNatural Resources CanadaMcMaster University
KeywordsScanning electron microscopeCorrosionMaterials scienceX-ray photoelectron spectroscopyAqueous solutionFocused ion beamAlloyAnodePolarization (electrochemistry)IonPorosityMetallurgyAnalytical Chemistry (journal)Composite materialChemical engineeringChemistryElectrode

Abstract

fetched live from OpenAlex

This technical note reports on relative differences in the physical and chemical nature of the surface films formed on AM60B during anodic polarization in an aqueous saline solution. Cross sections of the films prepared by focused ion beam (FIB) milling were analyzed using scanning electron microscopy coupled with energy-dispersive spectroscopy (SEM-EDS). The apparent breakdown potential (Eb) observed in the potentiodynamic anodic polarization curve is clearly associated with the onset of an accelerated form of localized corrosion. SEM-EDS analysis of FIB-sectioned surface films revealed that this accelerated form of localized corrosion coincides with a drastic change in the physical and chemical nature of films formed. The major physical change involves the transformation from a compact thin film to a much thicker film with significant poros-ity and cracking. The major chemical change involves the significant incorporation of Cl− into the much thicker, significantly porous, and cracked film.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0130.004

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 head0.030
GPT teacher head0.283
Teacher spread0.253 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations24
Published2012
Admission routes1
Has abstractyes

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Same venueCORROSIONSame topicCorrosion Behavior and InhibitionFrench-language works237,207