The effects of dietary net energy on grow-finish performance and carcass characteristics of market gilts managed with immunological suppression of ovarian function and estrus (Improvest)
Bibliographic record
Abstract
Abstract The objective was to determine the effects of net energy (NE) during the grow-finish period on live performance and carcass characteristics of market gilts managed with immunological suppression of ovarian function and estrus (Improvest®; IMP) compared with market gilts not managed with Improvest (CON). The 104-d study began when 1,008 gilts (11 wk old; average starting weight of 30.8 kg) were allocated by weight to 48 pens with 21 gilts/pen. Half of the pens were randomly selected to be managed with Improvest while the other half of the pens were not managed with Improvest. Three dietary programs differing in their NE were formulated over five dietary phases (according to standardized ileal digestible lysine requirements) to provide an average of 2,218 kcal/kg (low NE), 2,343 kcal/kg (medium NE), or 2,468 kcal/kg (high NE). The experiment was designed as a 2 × 3 factorial with main effects of Improvest management and NE. For the overall study period, there were no significant interactions (P ≥ 0.20) for average daily feed intake (ADFI), average daily gain (ADG), or Gain:Feed (G:F). There were also no significant interactions between Improvest management and NE (P ≥ 0.30) for carcass characteristics. However, IMP gilts consumed more feed (6.8% greater ADFI; P < 0.01), grew faster (5.0% greater ADG; P < 0.01), were less efficient (1.8% lower G:F; P < 0.01), heavier (3.5 kg hot carcass weight; P < 0.01), and fatter (1.9 mm greater backfat thickness and 1.26% less predicted lean carcass yield; P < 0.01). No difference (P = 0.21) in carcass dressing percentage between IMP and CON gilts was reported. For the overall study period, gilts fed low NE and medium NE diets consumed more feed compared with gilts fed high NE diets (6.8% more ADFI for low NE and 5.7% more for medium NE; P < 0.01), and gilts fed low NE diets grew 2.5% slower (P < 0.01) than gilts fed medium NE diets, while gilts fed high NE diets were intermediate and not different from the other NE treatments. This resulted in gilts fed Low NE diets being the least efficient (3.8% lower G:F than medium NE and 7.1% lower G:F than High NE; P < 0.01). Overall, these data indicate that typical Improvest response levels were sustained at each of the NE treatments evaluated in this study as there were no significant interactions for Improvest management and NE; however, consideration should still be provided to the known production impacts of low NE diets.
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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 | 0.000 |
| 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".