Georeferenced Information System as a Tool in the Quantification of Protection Areas
Bibliographic record
Abstract
The use of georeferenced systems has been widely used to obtain data on the quality of the environment, aiming to quantify how areas are being occupied, and how natural reserves are being affected as a result of human action. The Sanga Mineira microbasin belongs to the Paraná basin 3 and is considered one of the main water reservoirs in the municipality of Mercedes, with 120 properties in its territory, whose main source of income is agriculture and livestock. For the expansion of monoculture areas, many areas of Legal Reserve and Permanent Preservation are being destroyed by farmers, causing a series of environmental imbalances. Thus, through the above, the research aimed to quantify the areas of Legal Reserve (RL) and Permanent Preservation Area (PPA) in the Sanga Mineira microbasin, using the Georeferenced Information System (GIS), in addition to detecting the main changes that occurred as a result of the change in the Forest Code, to assess whether the new laws have helped to improve the sustainability of the environment. Methodological technical procedures the SPRING program was used to evaluate 97 properties, of which the three main land use classes were verified: Permanent Preservation Area, Legal Reserve Area and Total Consolidated Area. It was concluded that there was a decrease in the Legal Reserve areas and an increase in the areas of APP’s and Total Consolidated Area.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".