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Record W3083086023 · doi:10.1002/ces2.10068

Sinterability of macrocrystalline and cryptocrystalline magnesite to refractory magnesia

2020· article· en· W3083086023 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

VenueInternational Journal of Ceramic Engineering & Science · 2020
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Oxide Properties and Applications
Canadian institutionsUniversité de MontréalPolytechnique Montréal
Fundersnot available
KeywordsMagnesiteCalcinationMaterials scienceSinteringMagnesiumMetallurgyRaw materialChemistryCatalysis

Abstract

fetched live from OpenAlex

Abstract The purpose of this paper is to present the results of an investigation aiming at obtaining high quality sintered magnesia from two well‐known Chinese magnesite. Two types of natural magnesite have been considered, one macrocrystalline, from Liaoning, with the grain size of 60‐100 µm and one cryptocrystalline, from Tibet, with 2‐4 µm grains. Calcining characteristics to transform the two magnesite to caustic magnesia have been studied at first. Subsequently, the calcining‐sintering characteristics using a two‐ and a three‐step process for both raw materials at different temperatures have been determined. A DTA–TGA study reveals that the minimum endothermic peak during the calcining step differs by 28°C (624 vs 652°C between the crypto‐ and macrocrystalline magnesite). It has also been observed that by using a two‐step sintering process, it is possible to obtain a magnesia with a bulk density of 3.26 and 3.14 g/cm 3 , respectively, for cryptocrystalline and macrocrystalline, while with a three‐step process, it is possible to reach a bulk density of 3.48 g/cm 3 , even at a temperature <1750°C.

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 score0.110
Threshold uncertainty score0.383

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 science0.0010.000
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 head0.010
GPT teacher head0.242
Teacher spread0.232 · 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