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Record W2574781597 · doi:10.1063/1.4974408

Atmospheric leaching of nickel and cobalt from nickel saprolite ores using the Starved Acid Leaching Technology

2017· article· en· W2574781597 on OpenAlex
David Dreisinger

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

VenueAIP conference proceedings · 2017
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSaproliteNickelLeaching (pedology)SmeltingMetallurgyCobaltHydroxideEnvironmental scienceChemistryMaterials scienceInorganic chemistrySoil water

Abstract

fetched live from OpenAlex

There is great potential to recover nickel from below cut-off grade nickel saprolite ores using the Starved Acid Leach Technology (SALT). Nickel saprolite ores are normally mined as feed to Fe-Ni smelters or Ni matte smelting operations. The smelting processes typically require high Ni cut-off grades of 1.5 to 2.2% Ni, depending on the operation. These very high cutoff grades result in a significant portion of the saprolite profile being regarded as “waste” and hence having little to no value. The below cut-off grade (waste) material can be processed by atmospheric acid leaching with “starvation” levels of acid addition. The leached nickel and cobalt may be recovered as a mixed hydroxide (or alternate product). The mixed hydroxide may be added to the saprolite smelting operation feed system to increase the nickel production of the smelter or may be refined separately. The technical development of the SALT process will be described along with an economic summary. The SALT process has great potential to treat many Indonesian Nickel ores that are too low a grade for current technology.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.840
Threshold uncertainty score0.704

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 studies0.0010.000
Scholarly communication0.0000.001
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 head0.030
GPT teacher head0.273
Teacher spread0.244 · 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