Veal calves management in Québec, Canada: Part II. Association between passive immunity transfer at arrival and average daily gain
Bibliographic record
Abstract
The average daily gain (ADG) of veal calves is an important outcome to monitor for veal producers to maximize profitability. Transfer of passive immunity (TPI) is of paramount importance in dairy and beef calves. There is little information available that examine the relationship between TPI and ADG of veal calves in Québec. The objective of this study was to investigate the effect of arrival risk factors associated with lower ADG in milk and grain-fed veal calves in Québec. Between October 2017 and December 2018, a prospective cohort study was conducted on 59 batches of milk- and grain-fed veal calves in different geographic locations in Québec, Canada (n = 1729 calves). After arrival, thirty calves per batch were randomly sampled for estimating TPI using the Brix refractometer (serum threshold < 8.4 % for inadequate TPI). Throughout the production cycle, all health records of each batch of calves were extracted and used to quantify individual- and group-level risk factors. After the elaboration of a causal diagram using directed acyclic graphs, ADG was modelled through linear mixed models (LMMs) as function of categorical variables (individual inadequate TPI, arrival season, purchasing sites, and weights at purchase) and a continuous contextual variable (proportion of inadequate TPI in the batch). Also, the impact of morbidity (treated vs non treated) on ADG was investigated through linear regression model. Because performance and health data are typically underreported in commercial settings, data missingness was identified as a potential concern. Therefore, multiple imputation models were used. A total of 1084 calves had Brix % < 8.4 % giving a prevalence of 62.7 % of inadequate TPI. Individual calves with inadequate TPI gained 0.02 kg/d less than those with adequate TPI. Batch-level inadequate TPI prevalence was not associated with ADG difference in the sampled calves. Calves arriving to the facility during summer gained 80 g/d less than those arriving during fall. Calves treated at least once with antibiotic had lowered ADG by 7.2 kg throughout the production cycle compared to untreated calves. In conclusion, this study suggests that individual-level inadequate TPI assessed upon arrival in the facility, arrival season, and antibiotic treatments during the production cycle are associated with lowered ADG in veal calves.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".