Manganese Nodules in Chile, an Alternative for the Production of Co and Mn in the Future—A Review
Bibliographic record
Abstract
Given the active growth of emerging technology industries, it has become essential to have large quantities of critical metals to meet the current demand. In the Chilean mining industry, there is a depletion of high-grade mineral ores, and there is hence a need to increase production levels in the copper industry and diversify its market by extracting other elements. One of the strategies is to foster the production of lithium batteries, but the manufacture requires reserves of cobalt (Co) and manganese (Mn). Currently, Co reserves are not being exploited in Chile, and Mn production is almost negligible. This is due to the apparent shortage of high-grade ores on the land surface of the country. Given this scenario, the seabed manganese nodules are presented as a good alternative due to their high average grades of Co and Mn, which in turn would allow the growth of strategic value-added industries including lithium battery production. Chile’s current environmental regulations prevent the exploitation of marine resources. However, given technological advances worldwide, both in collection mechanisms and extractive processes, in addition to the needs generated from the future strategic plans, leads us to think about a project to exploit manganese nodules locally.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".