MétaCan
Menu
Back to cohort

Extraction of Ferronickel Concentrate by Reduction Roasting-Magnetic Separation from Low Grade Laterite Nickel Ore under the Action of Compound Additives

2022· article· en· W4286884521 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

VenueMATERIALS TRANSACTIONS · 2022
Typearticle
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsUniversity of Toronto
FundersNational Natural Science Foundation of China
KeywordsLateriteFerroalloyRoastingNickelMetallurgyMaterials scienceMagnetic separationNickel sulfidePyrometallurgySmelting

Abstract

fetched live from OpenAlex

Nickel is an important strategic metal in the world. As the sulfide ore containing high-grade nickel is increasingly depleted, the laterite nickel ore, which is rich in resources, has attracted people’s attention. In this paper, the reduction roasting-magnetic separation process is used to study the method of preparing ferronickel concentrates from low-grade laterite nickel ore under the action of composite additives (Na2CO3 and CaF2). The research results showed that when the ratio of additives Na2CO3 and CaF2 was 1:7, reduction temperature was 1250°C, reduction time was 60 min, magnetic field strength was 150 mT, and wet grinding time was 12 min, the nickel grade and recovery extent were 8.39 wt.% and 98.54%, iron grade and recovery extent were 67.70 wt.% and 71.73%.

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 categoriesInsufficient payload (model declined to judge)
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 score0.058
Threshold uncertainty score0.998

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)0.0030.000

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 head0.022
GPT teacher head0.263
Teacher spread0.241 · 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