Multivariate Geospatial Feature of the Soil Attributes of Archaeological Dark Earth in Novo Aripuanã, AM
Bibliographic record
Abstract
Changes in natural ecosystems for the use and management of soil can have negative consequences, favoring the appearance of areas susceptible to physical degradation. This work aimed to evaluate changes on the soil properties in Archaeological Dark Earth environments preserved under pigeon pea cultivation and pasture, using multivariate geostatistics technique. Sampling meshes were delimited with regular spacings with 88 sample points per mesh and then georeferenced. Soil samples and volumetric rings were collected in the layers 0.0-0.05 m, 0.05-0.10 m and 0.10-0.20 m, for the determinations of the physical attributes and soil organic carbon. The main components main components 1 (MC1) and main components 2 (MC2) were characterized by attributes related to the stability of the aggregates (geometric average diameter (GAD), weighted average diameter (WAD) and aggregate classes) and the related soil structure taxes, respectively, with a variability of soil attributes under forest influenced by values above the mean for both main components. Land use under pigeon pea little influenced the variability of the main components, presenting values of the attributes related to these components near the mean values, while the soil under pasture promoted influence only to the attributes related to main components 2 (MC2).
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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 | 0.002 |
| 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".