Analysis of the Impact of Land Use and Occupation on the Biophysical Variables of the Cerrado Biome in Southwest Goiano, Brazil
Bibliographic record
Abstract
The change in the use of natural vegetation by annual or perennial crops, sugarcane and fast-growing forests causes changes in the biophysical variables, and these changes can be monitored by remote sensing. The objective of this work was to evaluate, on a temporal scale, the impacts of land use changes on biophysical variables in the county of Santa Helena de Goias-Goias/Brazil. Between the years of 2000 to 2015 areas were identified for agricultural crops 1 (annual crops), water, agricultural crops 2 (sugarcane), natural vegetation, pasture and urban areas. The MODIS (Moderate Resolution Spectroradiometer) sensor products were selected for study: MOD11A2-Surface temperature; MOD16A2-Real evapotranspiration, MOD13Q1-Enhanced Vegetation Index and rainfall data from TRMM (Tropical Rainfall Measuring Mission). The geographic coordinates referring to the land uses were inserted in the LAPIG platform, searching the information of the biophysical variables referring to the selected pixel. The impact of land use change was evaluated by calculating the weighted average through the quantitative classification of the areas. It is concluded for the period of study that the index of average vegetation of the county had increase. There was an increase in the evapotranspiration volume of the county from 28% from 2000 to 2013 and the average surface temperature of the county showed a reduction of 2 °C in the period from 2000 to 2015.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 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".