Aplicação da estimativa espaço-temporal da tolerância à perda de solo no planejamento do uso da terra
Bibliographic record
Abstract
Application of space-time estimation of soil loss tolerance in land use planningBrazil is one of the main agricultural countries in the world and one of the only ones with the significant possibility expansion its agricultural area and productivity.The continuing need for the production increments leads to the occupation of less suitable areas for agriculture, with potentially increased soil degradation by erosion.In this context, soil conservation is an important variable and is partly related to the concept of land suitability.Erosion prediction models are important tools in agricultural planning, however, in many situations require reference tolerance values.As an alternative to reference values the aim of this study was to develop spatially the concept of Agricultural Lifetime (TVA) for the Brazilian territory, and apply it to situations possible for planning land use.The methodology was based on the adaptation of the public databases of the parameters of the TVA equation.Thus, we executed regressions, pedotransfer equations and calculations with Equation Universal Soil Loss (USLE).Applying the TVA concept to i) watershed analysis, through the median TVA values, ii) biomes analysis, Brazil is a signatory of the Convention on Biological Diversity of the United Nations (CDB) agreement, the given to conservation of soil a variable in locating new Conservation Units (UC), we sought to identify areas of lower potential agricultural lifetime (pTVA) as priority targets for creating new UC´s iii) expansion of agriculture analysis, was to identify the characteristics of agricultural frontier areas and not frontier in relation of pTVA.The main results were: i) it is possible to estimate soil parameters through regression, capturing between 36% (clay) and 60% (organic matter) of the total variance; ii) the combination of intense agricultural land use and shallow deep soils result in TVA lower, especially in the states of Minas Gerais, Santa Catarina, Rio de Janeiro e Paraná and in the Mata Atlântica biome; iii) the Amazon region and Bahia and Mato Grosso do Sul states showed up places with longer life; iv) the analysis by subbasins, which are in the best conditions are the basins of the eastern Atlantic and Amazon; v) the determination of priority areas for conservation was possible and resulted in six maps with more sensitive areas, generally associations wavy relief and shallow soils deep; vi) the analysis of the relation with agriculture in consolidated areas showed that there was no difference between the totals and that the areas occupied by agriculture, already in frontier areas tended to choose pTVA areas higher, with less risk of erosion.This phenomenon is expected by the theory of agricultural adjustment.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".