MétaCan
Menu
Back to cohort
Record W4387245319 · doi:10.1007/s11665-023-08762-z

Slurry Jet Erosion Resistance of VC-Containing Tool Steels: the Role of the Carbide Volume Fraction

2023· article· en· W4387245319 on OpenAlexafffund
O.A. Zambrano, Kidus Yoseph Tufa, Jiaren Jiang

Bibliographic record

VenueJournal of Materials Engineering and Performance · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicErosion and Abrasive Machining
Canadian institutionsNational Research Council Canada
FundersNational Research Council Canada
KeywordsMaterials scienceSlurryCarbideMetallurgyVolume fractionAlloyVanadiumVanadium carbideErosionJet (fluid)Volume (thermodynamics)Composite material

Abstract

fetched live from OpenAlex

Abstract Tool steels are well known to have superior strength and are claimed to have superior wear resistance than conventional steels in several scenarios. However, their behavior under slurry erosion conditions have been seldom studied. To this end, a set of tool steels, primarily from the CPM yV (y = 1, 3, 9, 10, 15) alloy family, was selected to understand the effect of hardness and vanadium carbide content on the slurry jet erosion (SJE) performance at different impingement angles. It was observed that the SJE resistance increases with the increase in the vanadium carbide volume fraction, but is independent of alloy hardness.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.478
Threshold uncertainty score0.167

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)0.0000.000

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 head0.006
GPT teacher head0.192
Teacher spread0.186 · 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

Citations10
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Materials Engineering and PerformanceSame topicErosion and Abrasive MachiningFrench-language works237,207