Use of self-organizing maps in the identification of different groups of reclamation sites in the amazon Forest-Brazil
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it