Bibliographic record
Abstract
Lithium (Li) is critical for developing electromobility, various modern technologies, and storing renewable energy. As a key component of lithium-ion batteries, Li production has increased more than eightfold over the past 15 years, with forecasts indicating a continued growth in demand. This underscores the importance of systematically exploring lithium resources, optimizing extraction technologies, and ensuring the sustainable management of this strategic raw material. The paper provides an overview of the main lithium deposit types, including Lithium-Caesium- Tantalum (LCT) pegmatites (e.g., the Tanco pegmatite in Canada, Bikita in Zimbabwe, and Greenbushes in Australia), greisens and enriched granites (e.g., Cínovec in the Czech Republic), and brines from closed-basin systems - especially within the so-called "Lithium Triangle" (Argentina, Bolivia, and Chile). Alternative resources are also examined, such as geothermal brines (e.g., Campi Flegrei in Italy), brines from oil and gas fields (e.g., in Arkansas and Texas), and lithium-enriched clays (e.g., in Yunnan Province, China, and the McDermitt Caldera, USA). These unconventional resources can potentially become economically significant in the future, complementing the traditional sources. This paper aims to characterize the various deposit...
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".