Índice de degradación ambiental agrícola: Un estudio en los municipios de Rio Grande do Norte
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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