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Record W2965730065 · doi:10.11159/mmme20.136

Characterisation of South African Chromite Middle Group Seams

2020· article· en· W2965730065 on OpenAlexvenueno aff
Mashudu Maruli, Willie Nheta

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicChromium effects and bioremediation
Canadian institutionsnot available
FundersUniversity of Johannesburg
KeywordsChromiteGeologyGroup (periodic table)Mining engineeringGeochemistryPhysics

Abstract

fetched live from OpenAlex

In this paper, detailed physical and chemical characterisation of the South African chromite Middle group seams (MG 1, 2, 3 and 4) was conducted to determine the effect it has in choosing the type of milling equipment and establish how the mineralogical characteristics vary in order to decide whether they should be processed separately or blended together. The chemical composition was analysed using XRF, mineralogical phases determined using XRD and elemental analysis as well as grain particle sizes were obtained from SEM and EDS. XRF results showed that the Cr 2 O 3 content in all seams was between 30 and 35% by weight and mostly associated with Fe, Si, Mg, and Al elements. Most dominant phases are that of chromite and magnetite in all the samples. The amount of chromium element by % weight in all seams ranged between 20% to 28 % as obtained from the EDS. The particle size of the grains in the ore ranged between 20 to 420m, with majority being between 150 and 200m. SEM showed that minerals were well distributed within the ore, with very few that were clustered together. From above mentioned mineralogical analysis that has been conducted thus far it was observed that all Middle group seams showed very similar characteristics, and blending them for processing would be most recommended, however they can also be processed separately depending on the availability of the ore.

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.

How this classification was reachedexpand

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 score0.004
Threshold uncertainty score0.425

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 head0.008
GPT teacher head0.171
Teacher spread0.163 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Mechanical, Chemical, and Material EngineeringSame topicChromium effects and bioremediationFrench-language works237,207