Petrology and Geochemical Characteristic of Granitoids From Guéra Massif in the Central Part of Chad: An Example of Mixing Magmas
Bibliographic record
Abstract
The granitoids of Guéra Massif are composed of biotite-granite, amphibole-biotite granite and gabbro-diorite and commonly contain micro granular mafic enclaves which vary from monzogabbro to syenite composition. They are metaluminous, high-K calc-alkaline to shoshonitic series. Gabbro-diorite rocks are magnesian while amphibole-biotite granites are magnesian to ferroan, and biotite granites are ferroan. They are enriched in LREEs relative to HREE and display negative anomalies in Nb, Ta and Ti. Fields relationships, petrology and geochemistry indicate that mixing and mingling processes could be more relevant for the genesis of granitoids associated to fractional crystallization. Thus, the presence of mafic enclaves of gabbro-diorite composition in the granites, the resumption of alkaline feldspar xenocrystals in the gabbro-diorites, as well as the linear correlation between the granites and the gabbro-diorites and the intermediate position of the mafic enclaves between the two formations, enable us to propose magmatic mixing as the major process that presided over the evolution of the Guéra granitoids. The delamination of the continental lithosphere during the post-collisional phase of the Pan-African orogeny would have caused the partial melting of the subduction-modofied mantle and lower continental crust and thus produced the magmas of the Guéra granitoids.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".