Voluntary feed intake in growing-finishing pigs: A review of the main determining factors and potential approaches for accurate predictions
Bibliographic record
Abstract
The ability of pigs to consume sufficient nutrients for optimal performance is an important consideration in commercial pork production. Nutrient intake levels are directly related to voluntary feed intake. Voluntary feed intake in pigs is influenced by several factors including environmental conditions (e.g. thermal and social conditions), animal status (e.g., age and physiological status), and feed and feeding conditions (e.g. bulkiness of the feed and feed form). Although the individual effects of many of these factors on voluntary feed intake have been investigated and quantified, little has been done to characterize their interactive effects. Under commercial conditions, voluntary feed intake is clearly influenced by multiple factors at any one time. Thus, there is a need for a means to accurately quantify voluntary feed intake in pigs as affected by the different interacting factors. Until quantitative effects of these interactions are established it is suggested that feed intake be monitored. This can be achieved by obtaining feed intake on representative groups of pigs. Key words: Voluntary feed intake, pigs, determining factors, prediction equations
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".