Main Impacts on Value of Milk Production in Different Regions from Rio Grande Do Sul, Brazil
Bibliographic record
Abstract
This paper analyzed the main impacts on the value of milk production in different regions (Development Councils) of Rio Grande do Sul, Brazil. For that, we used the structural-differential method known as method Shift Share effects with production, productivity, price of milk and production values. It is relevant to note that, as well as production, the dairy herd and the productivity underwent positive and significant changes in the analyzed period, in both federal and state level. The impact each variable causes on the total amount produced defines whether what is occurring is the use of resources or just an increase in the matrices. The main results show that some Councils, such as Fronteira Oeste, whose price-effect corresponds to in 40.65%, the lowest amongst all and denotes a lower market dependence. The most satisfactory productivity effect occurs in the Sul Council with 48.98%, inferring a higher production efficiency. For the herd effect, the highest matrices growth rate in production were found in the Metropolitano Delta Jacuí Council, with the value of 30.09%. However, for the purpose of value of production, the Rio da Várzea Council obtained the most significant value of 117.94%. Thus, the results enable understanding the bottlenecks and state regional needs in the sector, influencing economic decisions. The effect of the milk price was critical to increasing the value of milk production in the analyzed periods and the productivity effect showed mild effect on the value of milk production. Likewise, the herd effect was found in the analysis to generate less impact than other effects.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".