Análise comparativa do código de mineração do Brasil com outros códigos de mineração internacionais
Bibliographic record
Abstract
The new Brazilian Mining Code, Law 13.540/2017, replaces the 1967 Mining Code and presents alterations in the collection form of the Financial Compensation for Exploration Mineral Resources (CFEM), also called mining royalty. The calculating and incidence base of CFEM were modified, with the establishment of new billing indexes, according to the mineral in question. The actual Mining Code created the National Mining Agency (ANM) to replace the National Department of Mineral Production (DNPM). That new federal agency has the mission to regulate and inspect all the aspects that involve the mineral sector, since economic questions until socioenvironmental questions. The environmental questions in the new legislation are analysed, aiming the Brazil economic growth, in attention to the international market, without losing the perspective of sustainable environmental growth. The Brazilian Mining Code in comparing to the mining sector legislation of developed countries such as Canada, The United States and Australia, demonstrates that in those developed countries, the social and environmental responsibilities are shared among the federal government, provinces and territories. In Brazil, the mineral sector is controlled by federal government, while the environmental control and inspection can be shared by federal government, states and counties
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".