Magnetic phases of soils developed from igneous rocks in a climate gradient transept, Brazilian northern Amazonia
Bibliographic record
Abstract
Knowledge on magnetic phases and properties of magnetic minerals has wide applications in soils and in agriculture, by the possibility, and perspectives in application of rock magnetic methods in soil science and agriculture; however, their role in highly weathered soils is still unclear. We characterized the mineralogy of soils from Brazilian northern Amazonia, with emphasis on magnetic soils. Samples with varying weathering degrees were collected from four different localities, and their magnetic phases (MPs) were separated and subjected to the following analysis: X-ray fluorescence (XRF), X-ray diffractometry (XRD), and measurements of field and temperature magnetization. The chemical composition by XRF analyses revealed the predominance of Fe, Si, Ti, and Mn. The XRD analysis, using the Rietveld method, revealed the presence of hematite, goethite, maghemite, and magnetite as magnetic phases: The highest concentration of MPs was detected in an Fe-rich Typic Eutrudept (54% magnetite). Magnetization measurements of the magnetic phases showed the presence of magnetite, associated with hematite and goethite, with magnetization values and transition temperature characteristics of these minerals. The magnetization varied according to soil type, indicating different weathering processes. Soil magnetism varied as a function of parent igneous rocks, in the following order: diabase > basalt > granite. The results indicate that parent material and mineral weathering influence soil magnetism in a tropical climate.
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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 | 0.001 | 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".