MétaCan
Menu
Back to cohort
Record W3130712306 · doi:10.1080/03019233.2021.1882645

Dephosphorisation of hot metal containing moderate amounts of chromium with CaO–FeO <i> <sub>x</sub> </i> –Cr <sub>2</sub> O <sub>3</sub> –CaF <sub>2</sub> slag

2021· article· en· W3130712306 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

VenueIronmaking & Steelmaking Processes Products and Applications · 2021
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsUniversity of Toronto
FundersNational Natural Science Foundation of China
KeywordsChromiumSlag (welding)MetallurgyPrecipitationDissolutionMetalMaterials sciencePhase (matter)Chemistry

Abstract

fetched live from OpenAlex

With the extensive use of low-grade Cr-containing ore, the dephosphorisation issue of hot metal containing moderate amounts (0.2%-1%) of chromium is gaining more and more attention. To achieve dephosphorisation and chromium retention, a series of experiments on the dephosphorisation of hot metal containing moderate amounts of chromium were carried out at 1723 K. In the experiments, the crystallisation performance of the slag with various CaO/Fe2O3 ratio and Cr2O3 content, as well as the transfer of phosphorus and chromium between slag and hot metal at 1723 K were investigated. The results show that the dephosphorisation process goes through four stages: melting and precipitation, dissolution of refractory, continual oxidation, and phase transition. With the increase of CaO/Fe2O3 ratio, the chromium loss decreases while the dephosphorisation ratio first increases and then decreases. The addition of Cr2O3 promotes the precipitation of Fe(Cr,Al)2O4, which inhibits the dephosphorisation and chromium loss.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.057
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.200
Teacher spread0.191 · 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