Determining feeder space allowance across feed forms and water availability in the feeder for growing-finishing pigs
Bibliographic record
Abstract
Objectives: To evaluate a method of determining the optimal feeder space allowance for pigs. Materials and methods: Trial 1 used eight pens of 12 pigs to determine total eating time in pigs to estimate occupancy rates of a single-space feeder. Feed was provided in four combinations of feed form (mash versus pelleted) and water availability in the feeder (dry versus wet-dry). Eating behavior of pigs was video-recorded during both growing and finishing phases. Trial 2 used 560 pigs for the growing phase and 454 pigs for the finishing phase. Effects of feeder occupancy rate (< 80%, 95%, 110%, and 125% for the growing phase; 80%, 103%, and 125% for the finishing phase) on total eating time and growth performance were determined. Results: Both feed form (P < .01) and water availability in the feeder (P < .001) affected total eating time and, consequently, feeder occupancy rate. Pigs spent more time eating a dry mash diet than any other diet by water combination during both growing (P < .001) and finishing (P < .01) phases. As feeder occupancy rate increased to above 80%, either eating time (P < .05) or growth performance (P < .05) decreased. Implications: When testing levels of feeder space allowance and identifying the optimum, the designated number of pigs per feeder space should be determined according to feeder occupancy rates under different production settings. Optimal feeder space allowance should maintain both productivity and eating time of pigs.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".