PSV-22 The effects of flavoring agents on feeding behavior, feed efficiency, growth performance and temperament of newly arrived feedlot cattle
Bibliographic record
Abstract
Abstract Ninety steers (259.9 ± 36.18 kg BW) were used in a 56-d experiment to assess the effects of flavoring additives on feeding behavior, feed efficiency, growth performance, and temperament of newly arrived feedlot cattle. Steers were homogenously distributed by BW into six pens (15 head/pen) and pen was randomly assigned to one of 3 treatments (2 pens/treatment): a standard feedlot receiving diet (CT); or the same diet with a flavoring additive comprised of either sweeteners (SW) or a mix of basic tastes (MX) at 1 g/kg (Lucta SA, Barcelona, Spain). Pens were equipped with a feed intake monitoring system (Growsafe Systems, Airdrie, Canada), while BW and chute exit flight speed were measured bi-weekly during the study. Data were analyzed using a mixed-effects model accounting for repeated measures. There were multiple treatment × time interactions (P < 0.05), where DMI per meal was greater in SW than CT and MX on wk 3 and 5, respectively, and in MX than CT and SW on wk 3 and 7, respectively. The number of visits to the feed bunk per day was greater in MX than CT on wk 2, it was greater in SW than MX on wk 4, and it was greater in CT than in MX and SW on wk 4, and wk 7 and 8, respectively. The eating rate was greater in SW than MX on wk 4 and 5 and greater than CT and on wk 4. Although the cumulative responses for DMI, ADG and feed efficiency (FE; kg BW/kg DM) were not significant (P > 0.1), FE was greater in SW and MX than CT from 27 to 41 d. Despite these positive effects on FE, there was no feeding pattern associated with the inclusion of flavoring additives in the diet of receiving feedlot cattle.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".