116 Polyclonal antibody preparations from avian origin improves growth performance of beef cattle during the first fourteen days of the backgrounding phase
Bibliographic record
Abstract
Abstract This study aimed to evaluate the effects of feeding avian-derived polyclonal antibody preparation (PAP; CAMAS, Inc.) against Streptococcus bovis, Fusobacterium necrophorum, and lipopolysaccharides (40, 35, and 25% of the preparation, respectively) on growth performance of beef cattle during the backgrounding phase. From d 0 to 56, Angus crossbreed heifers (n = 80; 360 ± 60 kg of BW; 470 ± 26 d of age) and steers (n = 20; 386 ± 65 kg of BW; 465 ± 30 d of age) were blocked by BW and randomly assigned to 1 of 16 concrete-floored pens (108 m2), equipped with 2 GrowSafe (GrowSafe Systems Ltd., Airdrie, Alberta, Canada) feed bunks each. Animals received a common ad libitum diet (76% TDN, 15.9% CP, DM basis) with the addition of 1 (PAP1), 3 (PAP3), or 0 g (CON) of PAP per d. Feed intake was recorded daily and BW were obtained on d -1, 0, 14, 28, 42, 55, and 56, to assess changes in BW, ADG, DMI, and G:F. Based upon orthogonal contrasts (CON vs. PAP1, and PAP1 vs. PAP3), BW and ADG on d 14, and DMI from 0 to 28, and 0 to 42 were greater for PAP1 vs. CON (P ≤ 0.03), whereas PAP3 animals were intermediate (P ≥ 0.20). No differences in final BW, DMI, and ADG from d 0 to 56 were detected among treatments (P ≥ 0.22). In conclusion, feeding 1g of polyclonal antibody preparations against Streptococcus bovis, Fusobacterium necrophorum, and lipopolysaccharides in a backgrounding diet, improved growth performance in the first 14 d of feeding suggesting that feeding these PAP for longer than 14 d may not be necessary. The effects on subsequent feedlot performance when using PAP should be evaluated in future studies
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) | 0.000 | 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".