dataesr/scientific-paragraphs-categorization huggingface dataset:
Results A total of 79 farmers agreed to participate in the study (one farmer declined just before the time of interview). Main characteristics of farms from the study sample were similar to those of the target population (100 ± 59 and 99 ± 37 cows per herd, respectively; 34 % ± 6 % and 33 % ± 7 % of primiparous cows, respectively; 8 798 ± 1 122 and 8 789 ± 1 171 kg/cow/year, respectively) (data for the target population in 2019 came from databases of France Conseil Elevage and GDS Bretagne). Most of the contributing farmers (80 %) used at least one prevention programme at least part of the year (Table  2 ). Nutritional programmes were the most used (n = 50, 63 %). The MM programme was used on most of the farms (n = 42, 53 %). Among farmers using MM programme, no one used a mineral mix containing zeolite in order to decrease calcium absorption.
In addition, the analysis of our local database on forage mineral content supports the hypothesis that diet acidification was insufficient in a majority of farms (Supplementary Material S2). However, recent experimental studies indicated that a mildly acidified diet had positive effects (i. e. DCAD of À20 to À10 mEq/kg DM) on postpartum serum Ca  (Goff and Koszewski, 2018; Diehl et al. , 2018). These uncertainties could make it difficult for advisers to provide clear recommendations to farmers. Certain frequent management practices, such as feeding an acidified diet throughout the dry period (46 % of farms with an AcD programme), can jeopardise the preventative effect of this programme. Feeding an acidified diet for more than 3 weeks could dysregulate the secretion of parathyroid hormone (PTH)  (DeGaris et al.
Acknowledgements The authors thank  Benjamin Aubault  ( ONIRIS ) for participating in the study;  Maxime Leroy  ( Institut Agro ) for contributing to data analysis; and  Vincent Ballard  (CCPA),  Alexandre Budan  (Cargill),  Lionel Raulin  (Techna), and  Benoît Astruc  ( Vilofoss ) for contributing to the design of the interview and to data analysis.
Descriptive Statistics Mean litter size was 14. 75 piglets/sow (SD = 1. 9; min = 12; max = 16). The mean sow parity number was 3. 5. A total of 110 piglets (females n = 62, males n = 48) completed the prospective study. Seven piglets died during the lactation period, and one piglet died during the nursery period. Data from dead piglets were excluded from the analyses. Table  1  shows the overall means and standard deviations of the study outcomes. Concerning explanatory variables, the overall HCR mean was 0. 82 (SD = 0. 04; min = 0. 70; max = 0. 92). Proportions of piglets allocated into the covariate categories are presented in Figure  3. Table  2  presents linear regression estimates of the associations between HCRs and the outcome variables of this study. All models were adjusted by significant and controlling covariates, which are presented in each outcome section below.
Study population and Data collection The present study was carried out on patients with gastric diseases consulting at Ibn Rochd University Hospital Center, Casablanca (Gastroenterology and Oncology departments), aged from 18 to 86 years. Three biopsies (1 from antrum, 1 from fundus, and 1 from lesser curvature) were taken from each enrolled patient by endoscopy, from 2017 to 2020. Clinical information about demographic characteristics of participants including age, sex, living area, smoking, and alcohol habits was collected using a structured survey. All participants were informed about their inclusion in the study and agreed to it in writing form. The study protocol has been performed in accordance with the ethical standards of Helsinki and was approved by the committee of the Pasteur Institute of Morocco.
Statistical analysis Data entry and analysis were performed using RStudio software. The descriptive data are presented as frequencies and means ± standard deviations (SD). The differences between the groups were analyzed with Wilcox test for continuous variables, and Chi-square test or Fisher's exact test for categorical variables. The differences were considered significant at p < 0. 05. more detected in the rural area compared to the urban area in the same way for clarithromycin and metronidazole respectively (60. 1 % and 64% for rural area versus 12. 7% and 20% % for urban area) (Tables  1 2 ). However, the difference was not statistically significant.
, 2016) , although this belief has been challenged in a recent systematic review  (Beaver et al. , 2019). Farmers pioneering CCC systems are difficult to identify, as many European countries do not register this type of information in centralised databases. As such, there is a need to describe the range of practices that are used on CCC farms in Europe. More information on which type of practices that are used on CCC farms, and what aspects the farmers perceive as challenging with these systems is crucial for identifying what questions that remain to be answered on how to best manage dairy cows and calves together. The experiences of pioneer farms may help finding innovative solutions for common challenges in CCC systems, which will be important moving forward as the number of CCC farms is predicted to increase.
Material and methods For this study, we used a standardised quantitative questionnaire including both open questions to record continuous numerical data, and multiple-choice or checklist questions to record data related to factor variables and categorised numerical variables. Most multiple-choice questions were semi-open, providing an option ''Other, please specify", to allow identification of important factors or themes not originally included in the questionnaire. Recurring ''other" responses were recoded to allow for quantification; for example, some farmers mentioned building constraints, which was not included in the original questionnaire, as an important barrier when they were going to implement CCC on their farms. Farmers could choose which questions to respond to; therefore, the number of answers differs between questions.
The French research group had recently (March 2018) performed a similar questionnaire on 102 farms that allowed at least two days of cow-calf contact (Le  Cozler et al. , 2018). To avoid a low response rate from French farmers in the current study, data from the previous questionnaire were included for the 26 farms meeting our enrolment criterion. Questions in the two questionnaires were largely, but not fully, overlapping, leading to missing data for some questions. A version of the French questionnaire translated to English is available as Supplementary Material S2.
Data handling Data were entered, verified and processed by representatives from each country, using the Netigate platform (Netigate AB, Stockholm, Sweden). When entering the data, responses were translated to English. Data entries were reviewed, erroneous entries were corrected, and farms that did not meet the inclusion criteria were removed from the data set. Finally, the complete data set was downloaded as a csv-file for further analyses in R version 4. 0. 0  (Wickham, 2016; R Core Team, 2020; RStudio Team, 2020)  and as a xlsx-file for further analyses in Stata SE version 14 (Stata Corp LP, College Station, TX, USA). Scripts are available as Supplementary Material S3. Data were then analysed descriptively regarding possible differences between (1) countries, (2) conventional and organic herds, and (3) small and large herds, as well as (4) different CCC durations.
For comparisons related to herd size, the median herd size per country was calculated from the farms enrolled in the study. The farms were subsequently categorised as small (<median herd size), or large (median herd size). Responses to open and semiopen questions were used to classify the daily amount of cowcalf contact. Daily contact allowance was classified as either permanent contact (female calves had access to the cows except during milking), half-day contact (female calves could access the cows either between morning and evening milking or vice versa), restricted contact around milking (female calves could suckle for a limited amount of time either before, during or after milking), or use of more than one of these strategies (most often depending on the age of the female calves). Regarding comparisons related to CCC duration, the data set was split into short (7-28 days), moderate (29-90 days) and long contact (>90 days).
Results Farms using CCC systems were identified in all consortium countries except Poland. Of 121 identified farms fulfilling the enrolment criteria (7 days CCC after birth), data were obtained from 117 farms as four Swedish farms declined to participate in the study. Thirteen of these 117 farms were removed from the data set during data cleaning. Ten of the 13 removed farms either did not milk the cows or only milked for subsistence, two farms did not let the calves suckle but kept them with adult animals from three weeks of age, and one farm had only one dairy cow (which was used as a foster cow for beef calves).
Study limitations It is currently unknown how many farms use CCC systems in Europe. Due to the difficulties in identifying farms that practise CCC, farms were recruited through outreach activities and farm collaboration networks. However, even if snowball referral was used as a mean to recruit more farms, few farms were actually enrolled through other farmers. We also included French farmers that had participated in a recent, largely overlapping survey in our data set to reduce the risk of non-response bias due to respondent fatigue in this consortium country. This choice resulted in missing data for questions that were not included in the French questionnaire. The enrolled farms should not be considered a representative sample of all CCC farms in the consortium countries, and inferences should be limited to our sample.
Acknowledgements We thank the participating farmers for their help and for patiently answering all our questions. We acknowledge the dedicated work of  Antonio Difalco ,  Carmelo Guardiano ,  Carmelo Scollo ,  Giuseppe Azzaro ,  Antonio Natalello ,  Rosario Petriglieri , and  Karin Jonsson , who helped with data collection and data entry. We acknowledge that part of the data presented in this article have been included in two MSc theses (Jonsson, 2019;Gundersen, 2020); the data have also been published in a limited form as a conference abstract (Eriksson et al. , 2021).
Financial support statement The partners of the ProYoungStock consortium gratefully acknowledge the financial support for this project provided by the  Swedish research council Formas  as the Swedish transnational funding body (contract number:  2017-01854 ), being partners of the  H2020 ERA-net project ,  CORE Organic Cofund , and the cofund from the  European Commission (Coordination of European Transnational Research in Organic Food and Farming systems , project  ID 727495 ).
Real-time data were collected by the farmers (sow body weight and backfat thickness, litter size, and piglet body weight) and by the smart feeders, and then stored in a relational database management sys-tem (RDBMS) for online operations (PRE only), and evaluation  (PRE and STD). At the end of the experiment, the historical data collected from the experiment were analyzed. Based on measurements at farrowing, weaning, and estrus, sow and litter response were first evaluated according to their feeding strategy. Daily observations were then processed with a nutritional model for the ex post assessment of nutrient requirements and the calculation of P and N balance over the lactation period. Finally, in order to evaluate the performance of the DSS, the predictions of the DSS's various components were evaluated against the observed values (PADG, DFI).
Description of the decision support system Components. The DSS handled individual data collection and management at sow and litter levels. The DSS was composed of a RDBMS (MySQL 8. 0) containing the different data related to the description of the experiment, the performance of sows and their litters, DFI recordings collected by the electronic feeders, and data produced by the DSS. Each sow was identified with a unique number. Entries related to lactating sows, litters, and DFI were accessible through a web interface (Django 3. 0), to enable farmers to access it in real time for data collection and verification. Entries related to DFI were automatically retrieved from the electronic feeder. The DSS included machine learning algorithms to process the collected data and make predictions. The first machine learning algorithm predicted PADG from sow parity and litter size, according to past farm performance.
Training the two predictive algorithms. In order to train the litter growth predictive algorithm, a database was built with data from 1,691 lactations collected at the farm between July 2019 and March 2020, according to the procedure described by  Gauthier et al. (2022). The database with an overall r 2 of 0. 45. To train the feed intake predictive algorithm, daily feed intake data were collected at the farm between January 2018 and July 2020 on a total of 3,712 sows. The sows' feed intake trajectory curves at the farm were extracted from the training database using the k-Shape learning algorithm  (Paparrizos and Gravano, 2016) , associated with k = 2, selected as being the best cluster value according to Silhouette and Calinski Harabasz scores  (Gauthier et al. , 2021). The mean feed intake in the training database was 5.
78 kg. Online daily prediction of feed intake first required each PRE sow to be assigned to the closest feed intake trajectory curve, which had been previously identified by means of the shape-based distance (Paparrizos and Gravano, 2016). One-day-ahead feed intake was then predicted from the feed intake values of the sow in the two previous days, according to the method used by  Gauthier et al. (2021). On day 1, when there was no previous feed intake information available, the prediction was replaced by 2. 43 kg, the mean feed intake in the training database. On day 2, the online prediction was computed according to the closest feed intake behavior and the real feed intake on day 1.
Online process. The software was developed using Python 3 (Python Software Foundation, Beaverton, OR), and used cron (Unix) software for daily scripts automation. Computations that changed the feed composition were planned to take place between midnight and the first meal of each day, which occurred at 06:00. During the computation process, the individual feed intake from the previous day was first collected to assess the true nutrient intake and predict the next feed intake for the coming day. Changes in the number of suckled piglets, due to possible piglet mortality or fostering, were used to trigger a new prediction of the milk nutrient output. Based on these predictions, nutrient requirements were then predicted, and the optimal blend between High and Low diets was computed in order to meet the predicted requirement in SID Lys.
The sows were crossbred Landrace x Large-White (Line 276, Fast Genetics, Saskatoon, Canada). The PRE sows received a variable proportion of High and Low diets, as determined by the DSS, while STD sows were given the STD diet obtained by mixing High and Low diets (55% and 45%, respectively), which corresponded to the standard commercial lactation diet. High, Low, and STD diets were iso-caloric on a net-energy basis (10. 57 MJ/kg) but were different in terms of AA and mineral concentration. A detailed composition of the diets and their nutrient values is given in Table  1  on an as-fed basis. Animal response measured during lactation in sows and piglets was entered into the database through the web interface. On the day of farrowing, sow parity, body weight, and backfat thickness were recorded, and litter size and individual piglet weight were measured.
Evaluation and statistical methods Evaluation and statistical analyses were conducted using Python 3, with statsmodels (0. 12. 1), and SciPy (1. 3. 3) packages (Figure  1 ). An ANOVA was first carried out to evaluate the effect of the feeding strategy on sow and litter performance, with results considered statistically significant when P < 0. 05. Tested variables were related to sow (body weight and backfat thickness before farrowing, after farrowing, and after weaning, and weaning-to-estrus interval) and litter performance (sizes and weight at birth and at weaning, and average daily gain of piglets and litter over lactation). Sow body weight after farrowing was computed from body weight before farrowing and piglet weight at birth, in line with  Dourmad et al. (1997). Ex post requirements were evaluated after lactation with a nutritional model  (Gauthier et al.
, 2019)  from individual data on sows and litters collected during the experiment. An ANOVA was then carried out to evaluate the effect of treatments on ex post requirements and intakes. All statistical analyses were calculated based on a statistical significance cut-off of P < 0. 05. Tested variables were ex post ME, SID Lys, and STTD P requirements and intakes, DFI, and the percentage of High feed delivered. Daily nutrient supplies were compared to ex post nutrient requirements, on daily and weekly timescales. A global comparison was performed between precision and conventional feeding strategies to assess differences in N and P balances. Each balance was calculated considering the total ingestion of nutrients minus the amount of nutrients exported in milk. N mobilization was also taken into account, considering it as a source of nutrients as N ingested, and assuming that 15% of body weight loss consisted of proteins.
The analysis of ex post requirements also indicates 1) that SID Lys requirements may have been higher than predicted, and 2) that the daily balance in SID Lys between intake and requirement was generally slightly negative. This is even more important if we consider that the potential SID Lys requirement is the one observed with the standard feeding strategy, in which nutrient supply is likely to exceed sows' nutrient requirements. Given the components of the DSS, both an overestimation of feed intake and an underestimation of litter growth could result in an underestimation of the AA requirements. Because feed intake tended to be slightly underestimated, the underestimation of litter growth is likely to be the main reason. This is partly related to the fact that the litter performance in the database used to train the predictive algorithm is slightly lower than the performance achieved during the experiment.
As discussed in the Figure  5. Influence of the stage of lactation on observed feed intake (precision and standard feeding strategies), and predicted feed intake (precision feeding strategy). following subsection, careful attention must be paid to training the algorithm to predict litter growth, which is highly sensitive to the training database. From a dynamic point of view, the DSS made it possible to better take the variability in nutrient requirements into account over the lactation period. Except for the first day when there was no prediction available, the percentage of High feed strongly increased during the first week of lactation and subsequently decreased slightly. This is related to an increase in nutrient requirements due to milk production that is faster than the increase in the sows' feed intake capacity  (Hansen et al.
Acknowledgments The authors gratefully acknowledge  JYGA Technologies (Quebec, Canada ) for their assistance with smart feeders during the early stages of the experiment. This study formed part of a Ph. D. thesis in the # DigitAg  project ( ANR-16-CONV-0004 ), supported by the  French National Research Agency  in the " Investments for the Future" program ; and the  European Union 's  Horizon 2020 Research and Innovation program  (grant agreement no. 633531 ).
5X, 1X and 4X objectives as well as a Nikon U3 digital camera. Measurements were captured using the Nikon NIS software. Small features were examined, measured, and photographed using a Zeiss Imager Axio10 compound microscope (Zeiss, Germany) fitted with a Zeiss Axio cam 305 camera (Zeiss, Germany) and Zeiss Zen Blue elements (Zeiss, Germany) software. Measurements were based on ten specimens each from P. nelsoni and P. concinna, all collected near Gainesville, Alachua County, FL, USA. Morphological examination focussed on body size, relative size of the haptor, genital bulb diameter, number of genital spines, position of the vaginae in relation to body width and length, position of ovary, position of testis, presence and size of hamuli and haptoral sucker diameter.
PCR reactions were run twice and independently in a final volume of 25 µL comprising Buffer 1x, MgCl 2 1. 5 mM, dNTPs 0. 2 mM, primers 0. 4 mM, GoTaq Polymerase 0. 75 unit (Promega, France) and DNA (2 µL). PCR products were then pooled and sent to Gen-oScreen (Lille, France) for purification and sequencing with their respective forward and reverse PCR primers. Finally, we used Geneious software (Saint Joseph, MO, USA) to check chromatograms, and to read and edit resulting sequences. New sequences were deposited in GenBank with accession numbers OP784895, OP793140 to OP793161 and OP793434 to OP793461 for COI, and OP795734 to OP795746 and OP795805 to OP795807 for 28S. * refers to infected turtles that were surveyed for polystomes using a non-destructive approach with swabs rotated in the throat.
Phylogenetic and distance analyses within polystomes of the pharyngeal cavity New COI and 28S sequences, after primer trimming, were first aligned independently using Clustal W implemented in MEGA version 7  [19]  under default parameters  [41]. Only those characterizing polystomes of the oral cavity were kept at this stage. All these sequences were subsequently aligned with other COI and 28S sequences of distinct polystomes species retrieved from GenBank (Table  4 ). These sequences characterized polystomes of the oral cavity with the exception of Fornixtrema palpebrae (Strekov, 1950) of the conjunctival sacs and Polystomoidella whartoni Price, 1939 and Uropolystomoides malayi (Rohde, 1963) of the urinary bladder, that were used for outgroup comparisons after Du Preez and Verneau  [8]. In the final COI and 28S alignments, when identical sequences were found from the sequencing of eggs and/or worm, a single sequence was kept for each distinct haplotype.
The COI phylogenetic analysis was conducted on a data set comprising 64 haplotypes and 396 characters which was considered a single partition. A GTR + I + G model was selected following the Akaike Information Criterion (AIC) implemented in Modeltest 3. 06  [35]. Six types of substitutions and invariablegamma rates with four gamma rate categories were therefore applied. On the contrary, the 28S phylogenetic analysis was conducted on a data set comprising 15 haplotypes and 1,370 characters also considered as a single partition. A GTR + G model was selected following the AIC, with six types of substitutions and gamma rates with four gamma rate categories. The Bayesian analyses were run using MrBayes 3. 04b  [14] , with four chains running for one million generations and sampled every 100 cycles.
Systematics of Uteropolystomoides, a monotypic genus infecting Pseudemys spp. Measurements obtained from the 10 polystomes collected from Pseudemys nelsoni (Table  5 , column 1) and the 10 collected from P. concinna (Table  5 , column 2), showed an overlap indicating that all specimens belong to a single species. We therefore combined the measurements from the two polystome samples into a single set of data with their range, mean, and standard deviation (Table  5 , column 3). In the molecular study, we obtained 55 COI sequences including 16 new haplotypes (H145 to H160) and 13 28S sequences including two new haplotypes (Hnuc36 and Hnuc37). The resulting Bayesian consensus trees for COI and 28S are depicted in Figures  3  and 4 , respectively. The COI tree shows 12 well-resolved lineages that each likely reflect a distinct parasite species.