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Record W4392935191 · doi:10.1080/01496395.2024.2329275

Development of alternate process flowsheets to recover an unconventional resource rare earth bearing placer garnet and sillimanite from Southeast Coast of India

2024· article· en· W4392935191 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

VenueSeparation Science and Technology · 2024
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsAdvanced Micro Devices (Canada)
Fundersnot available
KeywordsSillimanitePlacer miningBearing (navigation)ChemistryRare earthGeochemistryGeologyPlacer depositEarth sciencePaleontology

Abstract

fetched live from OpenAlex

In the present study unconventional resources bearing rare earth mineral garnet and sillimanite have been attempted to recover using different alternate flowsheets. The best commercial grade garnet and sillimanite products are obtained from total heavy mineral concentrate recovered by using a spiral concentrator. The garnet product obtained after cleaning with spiral followed by a magnetic separator has a garnet grade of 98.8%, with a recovery of 97.2% and 4.13% overall yield from the bulk sample containing 4.5% garnet. In case of sillimanite also the spiral material is cleaned with a magnetic separator and followed by flotation yields a sillimanite material containing 98.6% sillimanite grade, with a recovery of 95.3% and an overall yield of 3.9% from most feed sample contain 4.0% sillimanite. Therefore, it is recommended to use gravity to remove the gangue ore before it enters the concentrator and to use magnetic equipment for garnet and flotation to obtain commercial grade sillimanite. Rare earth elements can be recovered from this level of garnet and sillimanite.

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 score0.079
Threshold uncertainty score0.295

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.015
GPT teacher head0.284
Teacher spread0.269 · 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