Índice de degradación ambiental agrícola: Un estudio en los municipios de Rio Grande do Norte
Bibliographic record
Abstract
This study aimed to verify the level of environmental degradation in the municipalities of the State of Rio Grande do Norte, using the calculation of the Agricultural Environmental Degradation Index (IDAA) as a proxy. For this, the factor analysis method was applied. To investigate the similarity between the municipalities of Rio Grande do Norte, according to their propensity for degradation, cluster analysis was used. The data were extracted from the 2017 Agricultural Census. The results showed that the State presented an average index of 25.59%, that is, about a quarter of the sample shows a tendency towards environmental degradation. However, most municipalities fell into low (38%) and medium (41%) levels, while 21% revealed high rates. Expenditure on agricultural pesticides, fuels and lubricants are among the main indicators that induce environmental deterioration. It is concluded that becoming aware of the adverse effects of the means used in agricultural activity is essential to encourage strategies, especially on the part of the public sector, that balance increased productivity and environmental conservation.
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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 | 0.001 |
| 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 | 0.001 | 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".