A review of lithogeochemical dispersion haloes of LCT pegmatites, and their application to rare metal exploration, with special reference to lithium in an Australian context
Bibliographic record
Abstract
Studies of lithogeochemical dispersion haloes of rare metal pegmatites, particularly those of economic interest, are known to display strong development of haloes of rare alkali elements (Li–Rb–Cs), in particular lithium, into country rocks. The use of these haloes in exploration for pegmatites has had apparently little application in an Australian context, where it has the potential to assist exploration undercover by expanding the known ‘footprint’ of these mineral systems. Dispersion haloes have two parts: proximal (typically of centimetres to metres thickness), characterised by visible changes to mineralogy relating to metasomatism, and distal, where there are typically cryptic geochemical changes, but no changes in host mineralogy. Collation of global data have shown that the lithogeochemical haloes of large rare metal pegmatites can extend laterally at least 200 m, with a few localities giving indications extending several kilometres along strike. Lithium displays the largest haloes, with Rb and Cs typically being less extensive; other elements are mostly localised close to the pegmatite wallrock interface. These haloes are more extensive in mafic host rocks than other host-rock types. Mafic host rocks also provide the best geochemical contrast for rare alkali elements. Of the 22 datasets reviewed here, many are simply ‘proof of concept’ studies based on single sample lines and may not have had their outer limits defined. The actual distribution of these haloes is likely to be controlled by anisotropies that control host-rock permeability in the host units, with fluids emanating from the rare metal pegmatites being commonly channelled into structures or exploiting foliation or bedding. Regional-scale structures are likely to control kilometre-scale haloes. Sheet silicates and amphiboles are the most important hosts for these elements, which consequently accumulate in units or structures rich in these minerals. The actual distributions of these elements are, in most cases, likely to be irregular and complex anomalies of varying three-dimensional shapes, and thus ‘vectoring’ based on systematic changes in concentration away from a source should be treated with caution. It is important to note that given anomaly types do not indicate the presence of particular minerals, such as spodumene. The presence of these haloes in Australian Archean cratons is supported by dispersion haloes in saprolite and saprock units in deep weathering profiles and is potentially applicable to lag sampling over near fresh or unweathered host-rock units in exposed terranes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".