MétaCan
Menu
Back to cohort
Record W7058173270

Metal-slag reaction through a solid interfacial layer

2017· other· en· W7058173270 on OpenAlexaboutno aff

Bibliographic record

VenueLA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2017
Typeother
Languageen
FieldEngineering
TopicMagnetic Field Sensors Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsAluminateAluminiumLithium (medication)SiliconAlloyLayer (electronics)MetalSlag (welding)
DOInot available

Abstract

fetched live from OpenAlex

Liquid Cu-Ni-Al alloys and two Li(2)O-Al(2)O(3)-SiO(2) liquid slags were contacted for 1-8 min at 1300 and 1450 degrees C. Their interface was deformed by the formation of a lithium aluminate crust due to aluminum oxidation. The interfacial area evolved with time depending on the compositions of the liquids. This led to the formation of structures made of liquid metal sheathed by a solid lithium aluminate layer. These structures were studied from metallographic sections of quenched samples and from X-Ray radiographs in real time. Their formation was attributed to the semi-permeability of the lithium aluminate layer allowing the transfer of aluminum ions to the slag but impeding the reduced silicon from reaching the liquid metal. Indeed, at the end of the slag-metal reaction, the silicon produced by the reduction of silica by aluminum was not found in the Cu-Ni alloy but silicon-rich droplets were found on the slag side of the crust. An explanation of these phenomena is presented. (C) 1999 Canadian Institute of Mining and Metallurgy. Published by Elsevier Science Ltd. All rights reserved.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.024
GPT teacher head0.261
Teacher spread0.237 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueLA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)Same topicMagnetic Field Sensors TechniquesFrench-language works237,207