263 Improvest Improves Gilt Performance
Bibliographic record
Abstract
Abstract Improvest (Zoetis, Parsippany NJ) is approved in the U.S for temporary suppression of estrus in gilts. Estrus suppression has secondary performance benefits, including increased average daily feed intake (ADFI), which leads to greater average daily gain (ADG), greater final body weight (FW), and improved group uniformity. Based on 4 recent (2016 to 2021) peer-reviewed articles (Brazil, Belgium, and Spain) and 5 Zoetis studies (USA and Canada), this summary quantifies the arithmetic mean (average) percent increase and the 95% confidence interval for ADFI, ADG, FW and hot carcass weight (HCW) of Improvest-treated gilts compared with untreated control gilts. The confidence interval (CI) was calculated in Microsoft Excel based on the average, standard deviation of the population, and the number of data points (measurements) for each parameter. Results showed that Improvest-treated gilts had a 3.2% (CI: 2.78% - 3.71%) increase in FW and 3.1% (CI: 2.49% - 3.91%) increase in HCW compared with controls. Overall ADG was increased by 4.4% (CI: 3.53% - 5.31%), with an 11.7% (CI: 68.7% - 14.66%) increase after the second dose. Similarly, Improvest-treated gilts showed an increase in overall ADFI of 6.1% (CI: 4.59% - 7.53%), and 12.8% (CI: 11.41% - 14.13%) increase after the second dose. There were no significant differences in overall feed efficiency (FE) in any of the studies. The rapid increase in ADFI occurs 7-10 d after the 2nd Improvest dose. Feed supplies must be managed carefully during this period to avoid out-of-feed events and capitalize on the increased ADG. This summary provides producers with solid estimates of the performance benefits associated with estrus suppression in gilts. Improvest clearly demonstrated an increase in HCW in gilts across all studies. Strategic use of Improvest has the potential to mitigate slow growth and reduced ADFI that is seen in the summer heat. TU-07020
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".