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Record W2592130249 · doi:10.1039/c6ra28421c

Surface damage mitigation of Ti6Al4V alloy via thermal oxidation for oil and gas exploitation application: characterization of the microstructure and evaluation of the surface performance

2017· article· en· W2592130249 on OpenAlexaff
Naiming Lin, Qiang Liu, Jiaojuan Zou, Dali Li, Shuo Yuan, Zhihua Wang, Bin Tang

Bibliographic record

VenueRSC Advances · 2017
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsImpactUniversity of Alberta
FundersNatural Science Foundation for Young Scientists of Shanxi ProvinceTaiyuan University of TechnologyChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsMicrostructureMaterials scienceAdhesive wearTitanium alloyAlloyGallingAdhesiveMetallurgyThermalCharacterization (materials science)Composite materialWear resistanceNanotechnology

Abstract

fetched live from OpenAlex

Ti6Al4V alloy is a promising candidate for petroleum tube. However, low surface hardness, high/unstable friction coefficient, severe adhesive wear and susceptibility to galling are harmful for the direct application of Ti6Al4V alloy in oil/gas well.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.285
Threshold uncertainty score0.212

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.238
Teacher spread0.225 · 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 teacher head, 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

Citations63
Published2017
Admission routes1
Has abstractyes

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