Trace Element Geochemistry of Li-Rich Pegmatites in the Carolina Tin-Spodumene Belt, North Carolina, USA: Implications for Petrogenesis and Exploration
Bibliographic record
Abstract
Abstract The Carolina tin-spodumene belt, North Carolina, hosts one of the largest economic deposits of Li pegmatite ore in the United States, yet the petrogenesis of Carolina tin-spodumene belt pegmatites remains poorly understood. We use whole-rock and mineral trace element geochemistry to (1) evaluate the petrogenesis of Carolina tin-spodumene belt pegmatites, (2) compare their geochemistry to other Li-rich pegmatites worldwide, and (3) propose mineral chemistry indices for Li mineralization. Trace element modeling demonstrates that spodumene-bearing pegmatites are not related to the nearby Cherryville Granite through fractional crystallization, and rare earth element contents in plagioclase, garnet, and apatite indicate that spodumene-bearing pegmatites are also not derived from spodumene-free pegmatites. We prefer a petrogenesis in which both types of pegmatites and the Cherryville Granite are derived through similar, but individual, crustal anatectic events. Muscovite and K-feldspar K/Rb-Li systematics indicate that Carolina tin-spodumene belt pegmatites do not attain fractionation levels as high as those reached in the Oxford County pegmatite field in Maine or the Custer and Keystone pegmatite fields in South Dakota. Quartz and garnet Li abundances in Carolina tin-spodumene belt pegmatites are some of the highest in the world, and garnet rare earth element concentrations are the lowest. Contents of Ga, Mn, Ge, and Ti in spodumene allow for discrimination of pegmatites from the Carolina tin-spodumene belt, Maine, South Dakota, Canada, and Portugal. Based on this extensive trace element study, plagioclase, K-feldspar, quartz, muscovite, garnet, and apatite chemistry offer a comprehensive methodology to distinguish pegmatites with and without spodumene in the Carolina tin-spodumene belt, which may be useful in exploration for Li pegmatite ore worldwide.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".