Geochemical signatures and weathering rates in soils derived from different granites in contrasting climatic locations
Bibliographic record
Abstract
We studied the mineralogical properties and chemical composition of different granites using energy dispersive X-ray spectroscopy coupled with scanning electron microscopy to understand the relationship between granite signatures and soil characteristics, including weathering patterns and soil fertility status. The discriminant analysis (DA) was based on soil physical and chemical properties and was used to differentiate soils developed from I- and S-type granites across contrasting climatic conditions in northeast Brazil. The DA identified the highest values of organic carbon, clay and plant nutrients as key variables to recognize soil profiles derived from I-type granites. In contrast, the greater sand contents and Al saturation distinguished soils developed from S-type granites. These results were related to the mineralogical characteristics of each granite type, such as a high percentage of silica-bearing minerals in the S-type granites. The highest concentrations of K, Ca, Mg, and P in I-type granites were found in feldspars, amphiboles, and apatite. These elements account for the majority of nutrients derived in their soil profiles. However, it seems that the harsh conditions of the humid tropical environment equalized the effect of the rocks on weathering rates-the difference in chemical index of alteration is more extreme in the dry region.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".