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Effects of Applied Power on Temperature of Electromagnetic Levitation of Silicon and Silicon-Iron Droplets

2018· article· en· W2791351427 on OpenAlex
Bing Yi, G F Zhang, Pei Yan, Lei Gao, Bao Hua Shi, Zhe Shi, Yi Yang

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

VenueIOP Conference Series Earth and Environmental Science · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMagnetic and Electromagnetic Effects
Canadian institutionsUniversity of Toronto
FundersAnalysis and Testing Foundation of Kunming University of Science and TechnologyKunming University of Science and TechnologyNational Natural Science Foundation of China
KeywordsSiliconLevitationMaterials scienceMagnetic levitationPower (physics)OptoelectronicsElectrical engineeringPhysicsEngineeringMagnetThermodynamics

Abstract

fetched live from OpenAlex

In this paper, a technique for non-conductive silicon heating and conductive silicon levitation is described. This research focuses on studying the effect of applied power on temperature of droplets during phosphorus removal from Silicon and ferrosilicon alloys (24%Fe- 76%Si) by utilizing a refining process known as electromagnetic levitation with subjecting the levitated alloy to an argon-hydrogen gas flow. The effects applied power on temperature were observed and analyzed. The results of this investigation will use vacuum electromagnetic levitation technology solar grade silicon samples will be prepared from relative inexpensive raw material, metallurgical grade silicon.

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)0.000
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 score0.098
Threshold uncertainty score0.681

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 studies0.0000.002
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 head0.002
GPT teacher head0.181
Teacher spread0.179 · 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