Use of self-organizing maps in the identification of different groups of reclamation sites in the amazon Forest-Brazil
Bibliographic record
Abstract
Brazil has the third largest reserve of contained tin, around 12.3% of the amount produced in world, being a large part of these reserves located in the Amazon region. As a result of this mineral wealth, the Amazonian ecosystem has been suffering a rapid process of environmental degradation since the sixties. In this sense, given the mining adverse consequences to the environment, Brazilian Constitution obligate the land reclamation of degraded areas by mining and it has been performed by the majority of the mining companies. However, given the environment complexity and its relationship with the biological diversity, there is a great necessity of better understanding in assessment of evolution of these degraded areas in recovery. Thus, the present work had as objective identifying different groups of degraded areas in reclamation process by means of soil texture, biochemistry and vegetation indicators. The data was analyzed through Artificial Neural Networks (ANN) Self Organizing Maps (SOMs). The results showed four different groups and it was identified a relationship between the different textures soils as a result to the recovery method applied.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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) | 0.000 | 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".