In-situ zircon and cassiterite LA-ICP-MS geochronology and implications for granite-hosted Sn deposit models and exploration: Insights from the Cameroon Line
Bibliographic record
Abstract
Abstract Tin mineralization of significant economic importance occurs across the continental portion of the Cameroon Line (CL). Tin deposits therein occur as both primary and secondary (residual and alluvial) ore. Though the temporal and, by inference, the genetic link between Sn mineralization and the host granite had long been modeled and widely accepted worldwide, in the CL, however, the age of the granite hosting cassiterite is poorly constrained, preventing a robust assessment of the temporal and genetic relationship between the Sn mineralization and its host rock. Here, we present in-situ zircon and cassiterite laser ablation inductively coupled mass spectrometry (LA-ICP-MS) U-Pb data in order not only to constrain the age of the granitic rock hosting the primary Sn ore but also to bracket the time frame of Sn mineralization, with respect to the magmatic-hydrothermal evolution of the parental magma of the host granite. Zircon from two greisen-altered, cassiterite-bearing granite samples yield overlapping and concordant ages of 64.21 ± 0.59 Ma and 65.46 ± 0.95 Ma, respectively, which are also overlapping with regional granite magmatism in the CL (ca. 65–30 Ma). On the other hand, cassiterite, which is spatially associated with the Paleocene zircon, yields Lower Eocene ages of 54.99 ± 0.35 Ma and 56.08 ± 0.46 Ma. The ca. 10 Myr time gap between zircon and cassiterite suggests that the granite is a passive host not genetically related to the Sn mineralization, which may be linked to a younger, concealed intrusion of ca. 55 Ma. This finding contrasts with the most widely accepted petrogenetic model of tin granite, according to which Sn mineralization and the host granite are cogenetic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".