The Perception about Batch Management Production Systems among Pig Producers
Bibliographic record
Abstract
This research investigates and evaluates different management systems, based on experiences of professional pig farmers to assist pig farmers in their choice for an appropriate management system. Flemish (Belgian) pig farmers were asked to complete a survey concerning the characteristics of their farm and the applied batch management system (BMS). Hence, advantages and disadvantages of all management systems could be investigated. Results indicate that labour efficiency seems to be the major reason for applying a BMS compared with the continuous management system. The results also reveal a significant association (P < 0.05) between the type of BMS applied and the number of sows at the farm. Furthermore, the weaning age of piglets depends significantly on the applied BMS. Finally, results pointed out that, in general, a 4 wk BMS (4-BMS) is indicated by the farmers as a labour efficient and profitable management system, although farmers perceived a reduced biological performance when applying a 4-BMS because piglets are weaned early. More research is required to solve current disadvantages of a 4-BMS, such as early weaning, and to confirm the perception of the pig breeders by analysing economic and technical data of the farm in such a way that BMS can further develop and improve.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".