Spatial Analysis of Forest Fragmentation in the Atlantic Forest Bioma Areas
Bibliographic record
Abstract
The goal of this work was to calculate landscape ecology metrics using the R language, allowing the analysis of forest fragments under the Atlantic Forest domain located in the sub-basin of Arroio Jaquirana, Rio Grande do Sul, Brazil. For the mapping of the forest fragments, we used images from the REIS/RapidEye sensor dated 2016, and the classification was supervised through the Bhattacharya algorithm. The fragments were analyzed in seven size classes, to separate them and to calculate the landscape metrics it was used R language. The results attained demonstrated that the native forest occupied 34.01% of the study area, covering a total of 1,995 fragments, of which 93.43% were less than 5 ha. The highest values of edge and perimeter-area ratio were found in the small fragments indicating a greater edge effect, with the central areas of these remnants being exposed to the external matrix effects. Thus, it is concluded that the Atlantic Forest is highly fragmented and is extremely important to establish measures to minimize the effects and/or increase the connectivity between the fragments through ecological corridors using the smaller fragments, in addition, it makes necessary the development of public policies and research for the management of the region in order to preserve the remnants.
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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 | 0.001 |
| 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 | 0.000 | 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".