dataesr/scientific-paragraphs-categorization huggingface dataset:
Additionally, the FI for each day was calculated, and individuals with negative values were removed (6 individuals, as these animals were assumed not to be correctly identified). The DMC and DSDD records that were outside 4 SD from the mean were also removed (5 individuals, 36,727 observations). All traits were filtered to remove outliers were based on a z-score as described previously. Due to the heterogeneity of the residuals across time, we could not assume a normal distribution across time. Therefore, we applied the z-score outlier removal process described above, using only within-day variation. This process to remove outliers is similar to the approach followed by  Pietersma et al. (2006)  and  Hurst et al. (2021). The final dataset 1 consisted of 1,197,425 observations from 4,572 individuals.
The descriptive statistics for each trait are presented in Table  1. An initial linear model was fitted to the DMC trait. The residuals from a model (described later) fitted to the data were plotted against the predicted values (Supplemental Figure  S1 ). Heteroscedasticity was observed, which is indicative of heterogeneity of variance, or potential noise from drinking entitlement. The decrease in variance could be caused by the reduction of milk feed entitlement in a stepwise fashion from around d 32, which prevents calves from drinking ad libitum, thus reducing the milk consumption of calves that would otherwise be drinking. Consequently, we investigated the feasibility of the first 32 d being a distinct trait. After filtering, dataset 1 contained 699,832 observations from 4,428 calves, and dataset 2 had 311,733 observations from 10,076 calves, all occurring within the first 32 d.
Pedigree and Genomic Information Pedigree information was reported directly from the farm using the VAS Dairy Comp software (2022, VAS, Tulare, CA). A total of 23,395 animals were included in the pedigree, which included 11,975 calves and their ancestors. Out of 11,975 phenotyped calves, 6,542 had both dam and sire reported, 5,229 had one or the other, and 204 calves had no parental information. Pedigrees were trimmed using the "trimPed" function implement-ed in the "pedigree" package  (pedigree v. 1. 4. 2; Coster, 2022)  in the R software (R Core Team, 2022) based on the relationship with individuals with phenotypes. Pedigree branches that did not end in a calf with data were trimmed out. After trimming, 18,256 animals remained in the pedigree dataset. There were 10,703 calves with genomic information before the quality control.
Genetic material was collected by the producers, and genotype information consisted of 4 commercial SNP arrays containing  11, 405, 17, 557, 18, 819, or 30, 754 SNPs (Illumina, San Diego, CA) , which were imputed to a common set of 72,820 SNPs using the FImpute software  (Sargolzaei et al. , 2014) , as reported by  Chen et al. (2023)  and  Pedrosa et al. (2023). Quality control of the genomic information was done by removing the following: (1) duplicated SNP (markers at the same genome location); (2) duplicated individuals; (3) SNP located on non-autosomal chromosomes; (4) SNPs with an extreme departure from expected heterozygosity levels (>0. 15); (5) SNP with call rate <0. 90; (6) SNP with minor allele frequency <0. 05; and (7) animals with call rate <0. 90. A total of 57,019 SNP and 10,076 animals with genomic and phenotypic information remained for further analyses.
Systematic Effects Both datasets shared similar explanatory variables. The systematic effect of time of day of feeding was split into 4 factor levels (early morning, late morning, early evening, and late evening), based on a 24-h clock starting at 0000 h and ending at 2400 h, which were split evenly on a 6-h basis. The effect variable of feeding day of calf started at d 1 and ending at d 60. The effect of feed entitlement is described in detail in the Phenotypic Data Summary section. PVMC and DMC followed a non-normal distribution due to calves having a change in entitlement every 10 d, increasing from 2 to 3 L every 2 h, then at age 32 d dropping to 2 L/d at weaning. Therefore, the reported feed entitlement was included as a main effect for all traits previously described. The systematic effect of birth weight was reported in kilograms.
The effect of BRD incidence was determined by counting the number of treatments given during the recording period. The effect of pen density was calculated based on the number of calves per pen on the specific date of the observation using the precleaned dataset. Because dataset 2 only had information about the feeder that serviced 2 pens, the 2-pen average was taken. Although most animals stayed with their feeder throughout the entire period, others were moved to another pen with new pen mates, and this was accounted for in the analyses. The effect of dam parity was coded as 1, 2, or 3+ (due to a limited number of dams above  third parity). The effect of average THI was reported for the observation day. The variance traits and daily datatype contained the same systematic effects, except the time of day of observation.
org/ 10. 6084/ m9. figshare. 25099910. v1;  Graham, 2024). Statistical Analyses. Multiple analyses were performed with the goal of understanding the genetic background of the traits of interest. All the analyses were performed using the Blupf90+ suite of packages  (Aguilar et al. , 2018). First, the datafiles were prepared with the renumf90 package. A REML analysis was performed using the Blupf90+ package  (Aguilar et al. , 2018)  with a convergence threshold of 1 × 10 -12. The models were evaluated using the expectation-maximization algorithm REML for the first 200 iterations and then reverted to the average information REML if convergence was not attained  (Aguilar et al. , 2018). The single-step genomic BLUP method was used to estimate breeding values based on an inverted H matrix as previously defined by  Christensen and Lund (2010)  and  Legarra et al.
(2009). The linear animal models had a general form: y = Xb + Zu + Wp + ε, where y is the vector of the phenotypic records for each trait; b is the vector of fixed effects, including time of day of feeding only for dataset 1, number of visits only for dataset 2, feeding day, birth weight in kilograms, dam parity, treatment incidence, pen density, THI, feed entitlement, and contemporary groups as shown in Table 1; u is the vector of direct additive genetic effects where u H ~, ; N u 0 σ 2 ( ) p is a vector of animal permanent environment effects where p I ~, ; N p 0 σ 2 ( ) X is the inci- dence matrix for the fixed effects; Z is the incidence matrix for the direct additive genetic effect, W is the incidence matrix for the permanent environment effect, and ε is the vector of residuals Genetic correlations between all traits were estimated through a bivariate model fitting the same systematic effects described for the univariate models, but excluding effects that would adjust for the trait that the correlation was being made with.
Phenotypic Data In this study, we derived 11 calf feeding and BRD treatment traits and then estimated their genetic parameters to assess the feasibility of using these traits as indicators for animal health. The summary statistics for all phenotypic variables evaluated are provided in Table  2. To our knowledge, this is the first study presenting genomic-based genetic parameters for calf feeding behavior traits derived from AMF data recorded in preweaning Holstein calves. In dairy cattle, more focus has been given to understanding traits relevant to lactation performance and health in robotic milk-ing systems  (Wethal and Heringstad, 2019; Dechow et al. , 2020; Piwczyński et al. , 2021; Chen et al. , 2023; Pedrosa et al. , 2023). However, early in life variables may be used to predict future performance and health in a dairy animal once they begin lactation.
Drinking Speed and Feeder Visits The drinking speed had a mean (± SD) of 0. 6 L/min ± 0. 20 with a range of 0. 003 to 1. 67 L/min. Studies have reported that calves maintaining higher levels of drinking speed are less susceptible to BRD  (Knauer et al. , 2017; Morrison et al. , 2022). We found that the drinking speed increased linearly across the age of the animal on average, which was reported by  Montes et al. (2023)  using the same dataset, showing that as the calf grows, it can consume more milk in a shorter period of time. The mean TNV was 9. 55 ± 6. 50 daily visits, with an observed range of 1 to 81 visits, whereas the mean DNRV was 6. 15 ± 3. 67 visits, with a range of 1 to 31 visits, which is in line with other studies that found fewer rewarded visits than TNV. Knauer et al. (2017)  reported that the number of unrewarded visits increased as the calves began the weaning period, which may explain a large amount of the total number of visits.
We found that the maximum TNV was >50, which is much greater than anticipated. This may be explained by calves continuously entering the feeder in a pacing manner, being pushed out and back into the feeder, or possible errors with the radio-frequency identification reader. Another reason for the large distribution is due to calves being given starter feed, which was not recorded. Some calves may begin consuming dry feed at an earlier age, lowering their number of visits compared with their peers. However, this information is not available in the datasets used. The number of rewarded visits was lower than the total number of visits. We found that DNRV had a maximum value of 30, most likely due to some calves only drinking a small amount and then resting, or due to errors with the recording system.
Another contributor to the disease spread is the pen stocking density. Svensson and Liberg (2006)  found that the optimal pen density for group housed dairy calves are no more than 10 animals. In the study by  Perrtu et al. (2023) , pen density was no more than 15, whereas the average for the Midwest United States was 17. 8, as reported by  Jorgensen et al. (2017). Our dataset had a pen density mean of 48. 57 ± 6. 58 animals, and the maximum pen density was 60 animals (Table  2 ). This may have contributed to the greater disease prevalence in the population. We also noted a difference in the incidence of BRD by year, where calves reared in 2015 had a lower incidence of BRD of 20% compared with 2019, which had an incidence of 81%. Another contributing factor may be the season, where winter generally has the highest incidence of BRD.
Variance Components and Genetic Parameters Variance component estimates are presented in Tables  3, 4 , 5, 6, 7, 8, and 9. To compare models for each trait, we assessed the BIC value from each model provided in Supplemental Table  S1  and ANOVA values. It must be noted that it is not practical to compare Akaike information criterion (AIC) or BIC values between the full and truncated datasets. This is due to function BIC = -2logL + p*ln(n), where p represents the number of parameters and -2logL represents the log-likelihood function that is dependent on sample size and n is the number of observations. Therefore, we compared the BIC values between models in each dataset group.
Milk Consumption Traits The traits most influenced by the truncation of the dataset were the milk consumption traits, where the estimate of heritability of PVMC increased from 0. 025 ± 0. 002 to 0. 040 ± 0. 003 and from 0. 069 ± 0. 005 to 0. 090 ± 0. 009 for DMC, when only the data from the first 32 d using a repeatability model were considered (Figure  1 ). The reason for the marked heritability difference is most likely because of a change in the correlation of the phenotypic variance over time due to the step-wise weaning methods mentioned previously. This causes the data distribution to fit a more parabolic shape that does not follow a linear pattern. When comparing the reliability of these 2 traits, we observe little change in how well the models perform when contrasting the changes in reliabilities for the repeatability models from 0.
were analyzed using 3 types of Legendre orthogonal polynomial RRM: quadratic, linear, and quadratic with heterogeneous residuals. We fitted both the full dataset and the 32-d truncated dataset. For the PVMC trait, we observed that the quadratic polynomial with 4 heterogeneous classes for the residuals fit the data best, with a mean heritability of 0. 07 (range: 0. 02-0. 11) and lowest AIC value. We also observed a wider range in the additive genetic variance when assuming heterogeneous residuals (range: 0. 009-0. 07) compared with considering homogeneous residuals (range: 0. 013-0. 05). This indicates that the RRM with quadratic polynomials may capture more of the genetic variation when heterogeneous residuals are included, possibly due to better biological fit. Care must be taken in interpreting the results for this methodology due to the difficulty of estimating boundary points using random regression models and could potentially be remedied in future studies by using B-spline models  (Meyer, 2005).
Drinking Duration and Speed Traits We observed a greater heritability for the duration traits compared to the milk consumption traits using repeatability models. DDPV had a heritability estimate of 0. 024 ± 0. 002 when fitting a repeatability model in the full dataset and increased to 0. 04 ± 0. 003 when evaluated using the 32-d truncated dataset. We observed similar improvements for DSDD with a heritability of 0. 067 ± 0. 004 for the full dataset and an increase to 0. 10 ± 0. 005 when truncating the data. A marked change in variance was noted for DSDD from 3. 27 ± 0. 18 to 6. 05 ± 0. 34 and 4. 67 ± 0. 11 to 8. 64 ± 0. 20 for additive genetic and permanent environmental variances, respectively, indicating that permanent environment is more important in early stages in life compared to across the full preweaning period.
This is further confirmed by the repeatability of the trait, which increased from 0. 16 ± 0. 003 to 0. 25 ± 0. 004 when the data was truncated. This may indicate that there are differences in calves' ability to acclimate to AMF. This also may be a direct effect of the step-wise weaning program due to the calves being fed less and therefore spending less time suckling. The average DS had an estimated heritability of 0. 08 ± 0. 004 when the full dataset was analyzed with the linear repeatability model, and had a marked increase to 0. 15 ± 0. 007 when considering the 32-d truncated dataset with the same model. This trait is derived from the total milk consumption divided by the daily duration of drinking sum and therefore is a ratio influenced by these 2 terms. We observed a linear behavior of this trait across feeding days, where DS increased as the calves aged.
Feeder Visit Traits The DNRV and TNV traits were assessed to indicate behaviors with a propensity to feed more often. The estimated heritability for the full dataset was 0. 032 ± 0. 0021 and 0. 051 ± 0. 004 for DNRV and TNV, respectively, for the repeatability model. However, the change was negligent or even decreased when the dataset was truncated at 32 d, with the heritability for DNRV  = - + ( )                σ , where i = 1, 2, … is the number of individuals, se is the standard error of prediction, F is the inbreeding of animal i, and σ u 2 is the additive genetic variance  (Mrode, 2014).
Graham et al. : GENETIC ANALYSES OF CALF TRAITS Table 7. Heritability estimates for feeding duration traits using Legendre polynomial random regression models; 2 polynomial (quadratic and linear) and a complete or 32 d of age subset dataset were assessed, and quadratic polynomial with heterogeneous residuals using multiple classes was also assessed on the complete dataset 1 Trait 2  = - + ( )                σ where i = 1, 2, … is the number of individuals, se is the standard error of prediction, F is the inbreeding of animal i, and σ u 2 is the additive genetic variance  (Mrode, 2014).
Graham et al. : GENETIC ANALYSES OF CALF TRAITS Table  8. Heritability estimates for behavior and lifetime bovine respiratory traits using Legendre polynomial random regression models; 2 polynomial (quadratic and linear) and a complete or 32 d of age subset dataset were assessed, and quadratic polynomial with heterogeneous residuals using multiple classes was also assessed on the complete dataset 1 Trait 2 The mean, minimum (Min), maximum (Max), and SD for variance components are provided in supplementary materials. Boldface signifies model with superior performance. 2 DNRV = daily number of rewarded visits (observations); TNV = total number of visits per day (observations); FI = feeding interval. F i i i u 2 2 2 1 1 = - + ( )                σ , where i = 1, 2, … is the number of individuals, se is the standard error of prediction, F is the inbreeding of animal i, and σ u 2 is the additive genetic variance  (Mrode, 2014).
and TNV being 0. 020 ± 0. 002 and 0. 050 ± 0. 003, respectively. Another trait that was assessed to predict behavior was FI which had a negligent heritability of 0. 008 ± 0. 0012 with the full dataset and 0. 023 ± 0. 0026 when the data was truncated at 32 d. We did find low but reasonable heritability for these traits when using RRM. The RRM with quadratic Legendre polynomials and heterogenous residuals for DNRV and FI did not converge. This is most likely due to the environmental variance of these traits not changing dramatically over time. Fitting the quadratic Legendre orthogonal polynomials with the full dataset was observed to have the best fit for all 3 traits. The greatest heritability was observed for FI of 0. 14 (range: 0. 02-0. 59), while DNRV had a heritability of 0. 12 (range: 0.
Future Studies Future studies should focus on leveraging computer assisted algorithms to clean datasets and reduce the complexity caused by erroneous values. Use of artificial intelligence or machine learning modeling can find trends in data with more precision and would help reduce the amount of noise within the data to better perform genomic prediction  (De Vries et al. , 2023). The addition of video imaging technologies would allow for capturing behaviors and better identifying false positive visits to the AMF. This data may also be used to predict overall resilience in calves using deviations in predicted trajectories to capture subclinical and misdiagnosed illnesses in calves. Furthermore, genetic correlations with future performance traits would help elucidate the importance of these traits observed at a young age on traits that are economically important.
ACKNOWLEDGMENTS This research was funded by the  Agriculture and Food Research Initiative Competitive  Grant number  2022-67021-37022  from the  USDA National Institute of Food and Agriculture (Washington, DC). We are grateful for the support received from the  Purdue College of Agriculture (West Lafayette, IN)  and  Purdue Ag Data Services  for supporting the development of the  Purdue Animal Sciences   Data Ecosystem  (PASDE), which is a platform for automatically gathering, integrating, and storing large-scale datasets from precision dairy farms to be used for this type of research. The authors thank  Homestead Dairy, LLC (Plymouth, IN ) and especially  Brian  and  Jill Houin , for inviting us on their farm and for allowing our research group to use their farm records. The authors have not stated any conflicts of interest.
from 2 data sources. Per-visit data were recorded beginning summer of 2019, whereas daily information was recorded beginning summer of 2015. 2 Dataset 1 = per-visit information beginning summer of 2019; dataset 2 = daily information beginning summer of 2015. 3 TOD = time of day, beginning at midnight of the observation day and categorized into 6-h increments; Age = calf age at feeding day, beginning at d 0 and ending at d 60; ET = entitlement allotted daily, or per visit basis; BRW = recorded birth weight; DP = parity of dam; PD = pen density at day of observation; TI = recorded treatment of illness; recorded as 0 through 5, where 1 is added for each subsequent treatment; THI = average temperature-humidity index of that day(NRC, 1971); TNV = number of visits observed during observation day; PMCA = period milk consumption average for each calf, PPDA = period pen density average for each calf; PDDA = period drinking duration average for each calf; CG = contemporary group effect, concatenation of year, season, and barn.
Graham et al. :  GENETIC ANALYSES OF CALF TRAITS Table6. Heritability estimates for milk consumption traits using Legendre polynomial random regression models; 2 polynomials (linear and quadratic) and a complete or a subset of the dataset (60 and 32 d of age, respectively) were assessed regression with heterogeneous residuals using varying classes were also assessed on complete dataset. Boldface signifies model with superior performance based on Akaike information criterion. 2 PVMC = milk consumption per visit (L/visit); DMC = total milk consumption per day (L/d). 3 QFull = quadratic Legendre polynomial with full dataset; LFull = linear Legendre polynomial with full dataset; Q32 = quadratic Legendre polynomial with subset data at 32 d; L32 = linear Legendre polynomial with subset data at 32 d; QHR = quadratic Legendre polynomial with full dataset using heterogenous residuals.
minimum (Min), maximum (Max), and SD for variance components are provided in supplementary materials. Boldface signifies model with superior performance based on Akaike information criterion. QFull = quadratic Legendre polynomial with full dataset; LFull = linear Legendre polynomial with full dataset; Q32 = quadratic Legendre polynomial with subset data at 32 d; L32 = linear Legendre polynomial with subset data at 32 d; QHR = quadratic Legendre polynomial with full dataset using heterogenous residuals, with (2) indicating second-degree polynomial or (3) indicating third-degree polynomial. 2 DDPV = drinking duration per visit (min); DSDD = daily sum of drinking duration (min); DS = daily average of drinking speed (L/min). 3 QFull = quadratic Legendre polynomial with full dataset; LFull = linear Legendre polynomial with full dataset; Q32 = quadratic Legendre polynomial with subset data at 32 d; L32 = linear Legendre polynomial with subset data at 32 d; QHR = quadratic Legendre polynomial with full dataset using heterogenous residuals.
3 QFull = quadratic Legendre polynomial with full dataset; LFull = linear Legendre polynomial with full dataset; Q32 = quadratic Legendre polynomial with subset data at 32 d; L32 = linear Legendre polynomial with subset data at 32 d; QHR (2) = quadratic Legendre polynomial (second degree) with full dataset using heterogenous residuals. 4Rel. = reliability calculated as r se.
Sensory analysis Sensory evaluations were conducted on LT steaks 3 days post-mortem, utilising the methodology outlined by  Patinho et al. , (2019 Patinho et al. , ( ,2021)). For data collection, the Compusense Cloud platform (Compusense Inc. , Guelph, Canada) was employed, with the information being recorded on Android tablets (Samsung Galaxy Tab E T560).
Quality parameters of beef at 3, 14, and 28 days post-mortem Instrumental data assessing colour, water-holding capacity, cooking losses, and shear force considered two pHu ranges and three refrigeration periods as sources of variation. ANOVA was then performed at a 5 % significance level, with pHu range as a fixed factor and animal as a random factor, using R software. A Principal Component Analysis (PCA) was conducted with combinations of pHu and storage time as individuals (rows) and instrumental data as variables (columns), using the Pearson correlation matrix in XLSTAT.