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Record W2961290943 · doi:10.1515/rams-2019-0012

Surface damage mitigation of titanium and its alloys via thermal oxidation: A brief review

2019· review· en· W2961290943 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.

Bibliographic record

VenueREVIEWS ON ADVANCED MATERIALS SCIENCE · 2019
Typereview
Languageen
FieldMaterials Science
TopicTitanium Alloys Microstructure and Properties
Canadian institutionsUniversity of Alberta
FundersNatural Science Foundation for Young Scientists of Shanxi ProvinceTaiyuan University of TechnologyChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsCorrosionMaterials scienceTitaniumMetallurgyBiocompatibilityThermal oxidationSpecific strengthTitanium alloyDegradation (telecommunications)Substrate (aquarium)AerospaceThermalComposite materialAlloyComputer science

Abstract

fetched live from OpenAlex

Abstract Titanium (Ti) and its alloys have been extensively applied in various fields of chemical industry, marine, aerospace and biomedical devices because of a specific combination of properties such as high strength to weight ratio, exceptional corrosion resistance and excellent biocompatibility. However, friction and wear, corrosion which usually occur on the surfaces of Ti-base components can lead to degradation in both properties and performance. Thermal oxidation (TO) of titanium and its alloys under certain conditions can accomplish significant improvements both in wear resistance and corrosion resistance, without special requirements for substrate geometries. In this review, the studies and applications of TO process in surface damage mitigation titanium and its alloys were reviewed and summarized.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.654
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.044
GPT teacher head0.328
Teacher spread0.283 · 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