Bibliographic record
Abstract
ABSTRACT Nickel laterite deposits are formed by the prolonged and pervasive weathering of Ni silicate-bearing ultramafıc rocks, generally in tropical to subtropical climates. The deposits can be further classifıed as hydrous silicate deposits (e.g., SLN Operations, New Caledonia), clay silicate deposits (e.g., Murrin Murrin, Australia), and oxide deposits (e.g., Moa Bay, Cuba; Cawse, Australia) on the basis of the ore mineralogy. The physical and chemical nature of a nickel laterite deposit is a function of many factors, including the composition of the parent rock, the tectonic setting, climate, topography (specifıcally, laterite morphology), and drainage. Nickel laterite ore is extracted using both selective and bulk mining methods in open cast mining operations. The mined ore has traditionally been processed either by hydrometallurgical leaching technology (pressure acid leach or Caron processes) to produce oxides of nickel and cobalt or mixed Ni-Co sulfıdes for market, or by pyrometallurgical smelting to produce ferronickel granules or nickel matte. However, recent advances in high-pressure acid leaching and continued testing of atmospheric leach technology should lead to a reduction in overall operating costs and increased exploitation of Ni laterite resources in the future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".