Impactos econômicos da redução do hiato de produtividade da pecuária de corte no Brasil
Bibliographic record
Abstract
Economic impact of cattle yield gap closing in BrazilBrazil is adopting agricultural, environmental, and forest policies, like the New Forest Code and the Paris Climate Agreement, based on the hypothesis that the increase of cattle productivity will release land for agricultural production, contribute for forest recover, and reduce greenhouse gas emissions derived from land use change.A computable general equilibrium model, named TERM-BR, designed and developed for the Brazilian economy, was employed to simulate the impacts produced by closing the cattle yield gap, which was estimated with recent data generated by geoprocessing methods.Indeed, it was verified that the intensification of cattle raising can release additional land for agriculture, it is able to avoid future deforestation in the Amazon and Brazilian Savanna, and at the same time it also releases land for agricultural production in other places of the country, but without consequences for its forests, since their areas already are well delimited and consolidated.It was also verified that the increase of cattle productivity contributes to reduce emissions in the agricultural and forest sectors, due to land use change that help to diminish future deforestation, however this policy enhances the total emissions of the Brazilian economy, because it increases economic activity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".