Performance and body composition of pigs reared under tropical conditions receiving two crude protein levels: conventional and precision feeding
Bibliographic record
Abstract
In this study the performance and the body composition of pigs under individual precision feeding (IPF) or conventional (CON) feeding systems, and fed high (HCP) or low (LCP) crude protein content diets under tropical conditions were evaluated. During 56 days, 60 castrated male pigs (39.6±1.29 kg of BW) were assigned to one of the four experimental treatments distributed in a 2×2 factorial design consisted of two feeding systems (CON and IPF), and two CP formulation (LCP and HCP). Pigs in the CON group received 1.02 and 0.85% of SID Lys during phases I (0-28 d) and II (29-56 d), respectively. IPF pigs received daily a personalized diet. The body protein and body lipid content were accessed using a DXA equipment at days 0, 28 and 56 of experiment. IPF pigs reduced their ADFI when fed a HCP diet during the first and second experimental phase (P<0.07 for FS × CP). Pigs receiving LCP diets showed greater ADG (P<0.01) than pigs fed a HCP diet. The body protein content of pigs fed a LCP during phase I (P=0.08) was greater than in pigs fed the HCP diet. Yet, CON pigs fed a HCP diet showed a compromised feed efficiency compared with CON pigs fed a LCP diet (P<0.07 for FS × CP). Under tropical conditions, the IPF technique is suitable even when HCP diets are used. However, to ensure a greater feed efficiency in CON systems, the use of industrial AA to reduce CP levels is recommended.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".