MétaCan
Menu
Back to cohort

Hydrated aluminum powder for direct alloying of steel and alloys - challenges of the future

2024· article· en· W4393139056 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

VenueInterConf · 2024
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsFédération des Comités de Parents du Québec
FundersShota Rustaveli National Science FoundationNational Science Foundation
KeywordsMaterials scienceMetallurgyAluminium

Abstract

fetched live from OpenAlex

The presented study considers and substantiates the possibility of increasing the efficiency of the technology of out-of-furnace, direct alloying of steel and alloys, through the combined use of hydrogen - and aluminothermic methods of reducing target metals from metal-oxide ore or technogenic powdered materials. To achieve this goal, it is proposed to use hydrogenated aluminum powder (1-5% AlH3), obtained as a result of hydro-vacuum dispersion of molten secondary aluminum, as a reducing agent. The morphology of the obtained powder particles containing different allotropic modifications of aluminum hydride is shown. Chemical features and advantages of the proposed process are discussed. The practical value and perspectivity of the development of this approach are argued.

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 score0.109
Threshold uncertainty score0.411

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 head0.011
GPT teacher head0.210
Teacher spread0.199 · 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