Chemical of Soils With Histic Horizon of Lakes and Riparian of the Savanna, Northern Amazonia, Brazil
Bibliographic record
Abstract
The savanna of the northern Amazon of Brazil is an ecosystem that presents lakes and riparian of buriti (Mauritia flexuosa L.) in its landscape. Although these ecosystems are protected by law, they are subject to changes by anthropization in their surrounding areas. The soils of lakes and on the banks of the buriti riparian of the savanna of Boa Vista, Northern Amazonia, are hydromorphic and, although they are important ecosystems for the environmental sustainability, they are little studied. Thus, the purpose of this work was to characterize the chemical composition of these soils and relate the results to the type of anthropization. Samples were collected at the margins of five riparian and three lakes in the 0.0-0.1, 0.1-0.2, 0.2-0.3, 0.3-0.4, and 0.4-0.5 m depth layers. Soil samples, in each environment, were taken from three points equidistant at 100 m. For soil characterization, pH in water, H + Al, Al3+, P, K+, Mg2+, Ca2+, and total organic C (TOC) were determined. The soils of lakes and buriti riparian are of high acidity, low natural fertility, and high toxicity by Al. Buriti riparian presented the highest levels of TOC but were also the environments most susceptible to the effects of anthropization, in which Bom Intento presented values of available P from medium to high by the interference of agricultural activities.
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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) | 0.000 | 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".