Electricity Consumption in the Pork Production Chain From the Western Region of Paraná State, Brazil
Bibliographic record
Abstract
The development of swine farming resulted in the specialization and transformation of the productive chain with direct impact on the agroindustry. The intensive swine production is representative, with relevant performance in the international scenario, with an expressive increase in volumes and values produced and exported, contributing significantly to the performance of the Brazilian trade balance. This performance is due to the technological and organizational advances of the last decades. The constant changes and advances that swine farming has been undergoing promote the search for new ways of raising pigs. There is a constant incorporation of new technologies and an uninterrupted reorganization in the production systems in the industry, aiming to follow the industrial progress with greater cost reduction and increased profitability. In this context, the objective of this study was to evaluate the consumption of electric energy in the productive process of pig termination in rural properties in the western region of Paraná. The study was conducted in three pig farms, where data were collected on the consumption of electricity in the production, slaughter and processing of pigs. The average specific energy consumption in the production of pigs in the termination stage was 0.0058 kWh kg-1, accounting for 1% of the process, while at slaughter it was 0.22 kWh kg-1, responsible for 38.22 kWh kg-1 % of consumption and processing of 0.35 kWh kg-1, accounting for 60.78%. Thus, results showed that the processing stage consumes the most energy within the pig meat production chain.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".