Physical, Chemical, Morphological and Mineralogical Characterization Surface and Subsurface in Hydromorphic and Non-hydromorphic Soil of the Central Amazon
Bibliographic record
Abstract
Amazonian soils have some physical, chemical and mineralogical differences, understanding the differences is important to understand the behavior of nutrients in the soil, especially phosphorus. The study was carried out in two types of soils: a Hydromorphic soil profile and a Non-hydromorphic soil profile in the Central Amazon region of Brazil, located in the metropolitan area of Manaus. Five depths were sampled: 0.00-0.05; 0.05-0.1; 0.1-0.2; 0.2-0.4; and 0.4-0.6 m. Physical, chemical, mineralogical and morphological attributes of both soils were studied. With the exception of clay, the levels of sand and silt remained stable in depth. The levels of organic matter gradually decrease in depth. The pH in water and in KCl, ∆pH, point of zero load, were similar between the surface and subsurface soils. Al3+, H++Al3+, CECt, CECT and clay activity were similar in Non-hydromorphic soil and increase in surface (< 0.2 m) and stabilize in subsurface (> 0.2 m). In both soils, the bases are reduced in subsurface (> 0.2 m). Aluminum and iron oxalate reduce with greater expressiveness in Non-hydromorphic soil. Aluminum and dithionite iron exhibit the same behavior. The levels of Goethite (Gt) and Hematite (Hm) are high in depth in the Non-hydromorphic soil and decrease in the Hydromorphic soil. There are sensitive distinctions between soils, due to the imposed edaphoclimatic conditions.
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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".