dataesr/scientific-paragraphs-categorization huggingface dataset:
Post-Reunion Period A vs. Post-Reunion Period B vs. Late-Reunion Period We also compared data collected in post-reunion periods A and B and in the reunion period. In the case of interspecific social behaviours, the median (IQR) was 4. 5 s (34. 75 s) in the pre-reunion and 0 s (18. 25 s) in the post-reunion period, but no significant differences emerged between the two periods (V = 1. 219, p = 0. 223).
Post-Reunion Period A vs. Post-Reunion Period B vs. Late-Reunion Period We also compared data collected in post-reunion periods A and B and in the latereunion period. 3. 2. 1. Individual Behaviours, Inactivity and "Not Observed" Medians (IQR) and statistical values of individual behaviours, as well as inactivity and "not observed", are reported in Table  4. Regarding individual behaviours, significant differences between the periods were found for attention, maintenance, exploration and locomotion (Friedman test: Q = 15. 891, p = 0. 0004; Q = 6. 309, p = 0. 0427; Q = 15. 617, p < 0. 0004; Q = 7. 924, p = 0. 0190, respectively), as well as for individual inactivity and social inactivity (Friedman test Q = 6. 633, p = 0. 0362; Q = 8. 857; p = 0. 0119, respectively). No significant differences were found for self-grooming, territorial behaviours and "not observed" (Friedman test Q = 3.
We obtained two estimates of the force of infection and its changes across time, by fitting (i) a catalytic model to age-seroprevalence data obtained from unmarked animals (cross-sectional), and (ii) a survival model to event time data obtained from recaptures of marked animals (longitudinal). Using both types of data allowed us to make robust inference about the temporal dynamics of the force of infection: indeed, there was evidence for a decrease in the force of infection between mid-2014 and late 2015 in both datasets. The force of infection was estimated to be reduced from 0. 115 year -1 [0. 074-0. 160] to 0. 016 year -1 [0. 001-0. 057]. These results confirm that transmission intensity decreased during the study period, probably due to management interventions and natural changes in infection dynamics.
, 2013). As France has been declared officially free of Brucella melitensis and Brucella abortus in domestic ruminants since 2005  (Perrin et al. , 2016a (Perrin et al. , , 2016b)) , the persistence of B. melitensis in a wildlife population raises serious public health and economic concerns. This led the French authorities to undertake several management interventions since the discovery of the outbreak in 2012  (Lambert et al. , 2018; Marchand et al. , 2017). Among these, two massive culling operations were carried out in autumn 2013-spring 2014 (n = 251 individuals culled, estimated population size before culling: 567, 95% CI [487-660]) and in autumn 2015 (n = 70 individuals culled, estimated population size before culling: 344, 95% CI ). In parallel, the population was monitored by Capture-Mark-Recapture (CMR) to collect demographic and epidemiological data every year.
Therefore, we were interested in estimating the FOI from the available datasets to get a complementary picture of the epidemiological dynamics in the study population. In ruminants, brucellosis is considered to produce lifelong infections, in particular in goats  (European Commission, 2001; Schlafer and Miller, 2007) , a species closely related to ibex. Brucella infection induces both humoral and cellular immune responses  (Carvalho Neta et al. , 2010; European Commission, 2001) , which remains detectable over long periods of time  (Godfroid et al. , 2010). The characteristics of brucellosis are therefore compatible with the use of catalytic models. The presence of antibodies detected by serologic tests is used to detect previous exposure to Brucella  (Godfroid et al. , 2010). However, a potential issue with using antibodies as a proxy of past exposure is that infected individuals could be missed if antibody levels wane over time and decrease below detectable levels  (Benavides et al.
Cross-sectional and longitudinal datasets In total, cross-sectional serological data were available from unmarked individuals tested between 2012 and 2018 (320 captured individuals, one found-dead individual and 42 culled individualsseropositive and 234 seronegative results; Table  S1 ). The first dataset we used was therefore age-seroprevalence data from these unmarked individuals (Fig. 2A , Table  S1  and Fig. S1 ). Among the seronegative individuals that were marked and released, 50 were recaptured at least once during the study period and were tested again for antibodies against brucellosis. The second dataset we used was therefore the follow up time (in days) between the first capture and the last recapture of marked animals. Because these animals were seronegative at the first capture, they either remained seronegative (n = 46) or became seropositive in the interval (n = 4 -Fig.
2B  and Fig. S1 ). This type of data is also called "event time" data, where the event here would be the seroconversion from negative to positive. Individuals that seroconverted are "interval censored", i. e. , the event of interest occurred between the last negative test and the positive test, but the exact time is not known  (Kleinbaum and Klein, 2005). Conversely, individuals that remained seronegative at the last recapture are "right censored", i. e. , the event of interest did not occur yet and may or may not occur in the future  (Kleinbaum and Klein, 2005). The longitudinal event time dataset covered the period 2012-2018, from first captures to last recaptures (Fig. 2B ), while the cross-sectional age-seroprevalence dataset covered the period from 1998 to 2018 (see below and Fig. S1 ).
The sample size for the longitudinal dataset (n = 50) was also much smaller than for the cross-sectional dataset (n = 363), because in the study population, most captures targeted unmarked individuals, in an attempt to test new individuals and maximise chances to remove infected individuals  (Calenge et al. , 2021). Indeed, marked ibex have more chances to be seronegative, as they already were during their last capture in a more or less recent past  (Calenge et al. , 2021).
The Akaike weights, which are relative model likelihoods normalized over the likelihoods of all possible submodels, were computed using the function aictabCustom of the AICcmodavg R package  (Mazerolle, 2019). The Akaike weight can be regarded as the conditional probability given the data of being the best model among the set of possible submodels  (Wagenmakers and Farrell, 2004 ). We selected a set of models for which the ΔAICc was below two  (Burnham et al. , 2011; Burnham and Anderson, 2002). Given that the selected models included the covariate year, we further explored the existence of temporal variation in the annual FOI by assuming a constant FOI with age but estimating different baseline FOI λ 0 for one, two or three periods of time to test our hypothesis. We therefore assumed that the FOI could be considered as time-independent on average for several years.
Estimating the FOI using longitudinal event time data As we were interested in the time until the occurrence of infection ("survival time"), we analysed our longitudinal event time dataset using survival models. We note T the random variable representing the time between the first capture and the infection event, in days. The survival function is the probability that infection has not yet occurred by time t (follow up time): Surv(t) = P(T ≥ t). Note that in our data, animals were captured for the first time at different dates, meaning that the follow up time had different origins and therefore was not equivalent to the calendar time (Fig. S1 ). The survival function can be related to the hazard function λ(t), i. e. , the rate at which the event occurs per unit time, at time t, using the following equation  (Kleinbaum and Klein, 2005) : Surv(t) = e - ∫ t 0 λ(u) du In our case, the event of interest is infection, and therefore the hazard function is the FOI.
Note that if we assume a constant hazard risk (FOI) with follow up time, the hazard function is λ(t) = λ, and the corresponding survival function is Surv(t) = e -λt (exponential model). We tested the assumption that the hazard function is independent of age and calendar time, using the exponential model from the function flexsurvreg of the flexsurv R package  (Jackson, 2016). We tested for the effects of age at last recapture (as a continuous variable, calculated as the age at first capture incremented by the number of years between first capture and last recapture) and year of recapture (as a categorical variable). Because of small sample sizes, we had to group years 2014 and 2015 on the one hand and 2016-2018 on the other hand. We were not able to take into account the effects of sex and socio-spatial unit because of small sample sizes.
Discussion The aim of this study was to estimate the FOI of brucellosis in the wild Alpine ibex population of the Bargy massif (France) between 2012 and 2018. This measure of transmission intensity could help assess changes in transmission dynamics after management interventions started in 2012, as a complement to existing seroprevalence studies  (Calenge et al. , 2021; Marchand et al. , 2017). Two massive culling operations were implemented in the population in 2013 and 2015. In addition, test-and-remove operations were performed during captures every year since 2012, with particularly high number of captures in 2015. We used two different datasets: age-seroprevalence data obtained from culling or first captures operations (n = 363), and event time data, derived from recaptures of marked animals (n = 50).
In both datasets, the model selection process supported an effect of time on the FOI, and returned estimates supporting a potential decrease of the FOI between mid-2014 and late 2015. The force of infection was estimated to be reduced from 0. 115 year -1 [0. 074-0. 160] to 0. 016 year -1 [0. 001-0. 057] in the age-seroprevalence data. Longitudinal samplings are difficult to obtain in wild populations and are therefore often not available  (Gilbert et al. , 2013). In our case, data from recaptured animals were available, but the sample size remained relatively low (only four seropositive among 50 recaptured animals), leading to large confidence intervals in the estimates of the FOI (e. g. [0. 015-0. 193] year -1 for 2012-2015). Although a higher number of recaptures may have provided a more complete dataset, it would have had negative impact in terms of disease management.
Model assumptions and infection-related mortality In both FOI models of cross-sectional and longitudinal data, the main assumption is the independence of the FOI with age. We did not find any support for age-dependency in our datasets (Table  1 ). Estimating the FOI from age-prevalence data also had the advantage of bringing insights on brucellosis-related mortality, for which we did not find strong evidence (Table  1 ). Our results did not detect brucellosisinduced mortality in ibex, similar to studies in bison  (Fuller et al. , 2007; Joly and Messier, 2005)  or elk  (Benavides et al. , 2017). We also postulated that the serological test has perfect sensitivity and specificity: due to the use of multiple serological assays, previous studies estimated the sensitivity of diagnosis protocol to be around 95%, with perfect specificity (expert assumption in ANSES, 2015;  Lambert et al.
, 2020). Test failure thus does not appear to be a high source of bias, however, in the case of lower quality tests, other modelling approaches could be investigated  (Bollaerts et al. , 2012; Buzdugan et al. , 2017). A few individuals in our datasets only had results from the rapid LFIA test used in the field (see Material and Methods). The analyses produced qualitatively and quantitatively similar results when using reduced datasets without these individuals (not shown). In a former study, seroprevalence was found to decrease in old individuals, especially in males  (Marchand et al. , 2017). It was hypothesized that this phenomenon could be explained either by a lower survival rates or by lower probabilities to test positive in older individuals infected by brucellosis  (Marchand et al. , 2017).
Temporal variation of the force of infection In  Marchand et al. (2017) , statistical analyses of seroprevalence data from 2012 to 2015 did not detect changes in seroprevalence before and after the mass culling of autumn 2013. Using a Bayesian approach to estimate seroprevalence,  Calenge et al. (2021)  found a strong decrease in the overall seroprevalence between 2015 and 2016. In the present study, both datasets suggest that the FOI decreased between mid-2014 and late 2015 (Fig. 3 ), which could explain the seroprevalence drop also observed. Our results support a decrease in transmission during the study period, probably due to management interventions and natural changes in the infection dynamics. Quantitatively, seroprevalence was halved between 2015 and 2016  (Calenge et al. , 2021) , while FOI as estimated using age-seroprevalence data was divided by approximately ten at the same period (0.
115 year -1 vs. 0. 016 year -1 ). In the process of estimating the seroprevalence in marked animals,  Calenge et al. (2021)  also estimated the FOI exerted on marked females in the core area, assuming it was proportional to the proportion of seropositive unmarked ibex with an active infection among the population of females in the core area. This analysis showed a decrease between 2015 and 2016, with estimates that were consistent with the values obtained with our longitudinal event time dataset where we did not make such assumptions on the relation between the FOI and active infections in the population  (Calenge et al. , 2021). Finally, an individual-based, stochastic SEIR mechanistic model, also showed a decrease in incidence between 2013 and 2016 in the whole population, and a low increase of new cases between 2016 and 2018.
Force of infection in marked and unmarked animals Our study aimed to compare the FOI between marked and unmarked animals. Most captures targeted unmarked individuals to maximise chances to remove seropositive individuals  (Calenge et al. , 2021). As a result, only a small number of marked animals were recaptured and a few cases of seroconversion were observed, leading to high uncertainty around the estimates of the FOI from the longitudinal event time dataset. Despite this uncertainty, the estimates from both datasets are comparable in each period  (2012-2015 and 2015-2018)  and support a potential decrease of the FOI of similar magnitude. These results indicate that the FOI is similar in susceptible individuals from both groups, and therefore that marked individuals have similar patterns of exposure as unmarked individuals.
Assuming similar FOI, a unified framework, using marked and unmarked animals, could be then further investigated to gain in precision of FOI estimates, even with relatively small longitudinal sample sizes, and better evaluate the requested sampling size of the monitoring  (Gamble et al. , 2020 ). An integrated framework combining cross sectional and longitudinal capture-recapture setups would also decrease the needed sample size in comparison with separate approaches  (Gamble et al. , 2020). It is worth noting that both our cross-sectional and longitudinal datasets are interval censored event time dataset: from birth to the positive test for the age-seroprevalence data or from negative to positive test for the longitudinal data. Therefore, future research could use both datasets in the same analyses, which will allow to increase the statistical power and to use more complex models of time-varying FOI.
Conclusions In conclusion, using two distinct methods and datasets, our analyses revealed a decrease in the FOI between mid-2014 and late 2015, possibly as a result of the successive management operations implemented since 2012. Estimating the FOI can therefore provide valuable insights on the epidemiological dynamics, as a complement to classical seroprevalence analyses. This is especially the case in the context of ongoing disease management interventions in wildlife populations, where the FOI is expected to change. Insights on the effect of managing on transmission dynamics can be obtained using rather simple models as we did here, provided that there is no strong evidence that the basic assumptions are violated. Comparing two different sources of data, cross-sectional and cohort studies, with appropriate models may contribute to provide robust inference about the FOI.
Acknowledgments The authors thank the two anonymous reviewers whose comments helped improve and clarify this manuscript. The authors warmly thank all the field staff who performed monitoring and management of the Alpine ibex population. They also thank the scientific experts of the ANSES working groups on brucellosis of Alpine ibex in the Bargy massif, as well as  Charlotte Dunoyer  ( ANSES; UERSABA's chief unit ) and  Jean Hars  (ONCFS). This study was coordinated by the  French Biodiversity Agency (Office Français de la Biodiversité ,  OFB, formerly Hunting and Wildlife Agency ,  Office National de la Chasse et de la Faune Sauvage, ONCFS). This work was performed within the framework of the  LABEX ECOFECT  ( ANR-11-LABX-0048 ) of  Université de Lyon  and of the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR).
The considerations, defined to address aspects critical in regulatory acceptance, are also rank ordered to reflect their relative importance in assessing the extent of supporting mechanistic data. Examples of the types of datasets associated with high, moderate and low confidence accompany each of the modified considerations in program guidance  (OECD, 2018). These designations are based on the extent to which evidence supports expected patterns across levels of biological organization and data sources, including studies in humans, animals and in vitro or in silico. For AOPs in the AOP-wiki, the extent of confidence for these considerations in the KE, KER and the AOP overall are also illustrated in a user-friendly summary graphical interface to guide appropriate application. The modified or tailored Bradford Hill considerations are "expert informed" and application driven, reflecting experience in regulatory application of mode of action analysis and evolution in the OECD AOP development program through collaborative interface of the research and regulatory communities.
They draw on accumulated experience to contribute to the delineation of the content of reporting templates for individual KE, KER, and the AOP overall. In addition to assessing confidence for each of the considerations as high, moderate or low, developers provide text rationales to justify their designations based on the provided examples for components and the AOP overall. The OECD Extended Advisory Group on Molecular Screening and Toxicogenomics reviews WOE descriptions internally for compliance with program guidance. Descriptions are reviewed subsequently by a team of external scientific subject experts and approved by the OECD Working Group of the National Coordinators of the Test Guidelines Programme and the OECD Working Party on Hazard Assessment. Specific definition of the modified Bradford Hill considerations and provision of examples of the nature of supporting datasets giving rise to different levels of confidence and associated templates for description of the relevant features of the supporting datasets contributes to the consistency and transparency of AOP descriptions.
Case Studies for applying SR methodology to AOP development The second theme-specific talk was given by Michelle Angrish, who presented a case study for applying SR methodology to AOP development. SR methods are a natural fit for AOP development, providing a mechanism for meeting the expectation that an AOP will include the most relevant and robust information. Even though practical application of SR methodology is labor intensive and costly, there are aspects of SR workflows (e. g. foundational methods and concepts, open-source and freely accessible tools, etc. ) that offer flexibility under the rubric of current AOP development guidelines. These advantages include leveraging pathway based systematic methods that add transparency, traceability, ease in updating, and reduced bias in selecting data and or information mapped to AOP concepts.
, 2010). Currently, AOP information is captured in a schema that maximizes the benefits established by the Findable, Accessible, Interoperable, and Reusable (FAIR) data principles by applying ontology-based annotations to concepts within the biologically structured AOP framework  (Ives et al. , 2017). Therefore, the pilot shifted toward a step-wise goal of literature inventory information digitization that would feed-back on the screening process to improve screening decisions. This step-wise approach meant that information captured in an evidence map (environmental science vocabulary) would require transformation into controlled vocabularies (unambiguous and non-redundant) that can be mapped to ontologies amenable to the AOP framework. Realizing that systematic review methodology is conducted in a step-wise structured manner that is amenable to technological innovation, strategies to address several advances and lingering challenges faced during application of computationally intelligent approaches are being explored.
For example, evidence maps provide the same level of consistency, transparency, and documentation as a SR, but present summaries of the evidence. Such summaries are useful for refining an AOP developer's approach based on the available evidence base and goals of AOP development. An additional challenge is developing the search strategy for finding data that will inform the evidence map, understanding that the broader the search, the more effort required to screen the retrieved results. In the context of AOP, the initial literature search strategy may consider the intended application of final AOP, resources as well as time, and tailor the screening criteria accordingly. For example, a AOP developer may start with a broad search strategy and then refine the search results based on the available information or identify additional strategies needed to cover gaps in the evidence map.
This includes temporal concordance where activity for the upstream KE precedes that of the downstream KE. In cases where chemical perturbations were examined, there is also dose concordance where the upstream KE is seen at lower doses than the downstream KE. The final consideration when evaluating an AOP is the essentiality of the KEs. While this is recorded for the AOP as a whole (i. e. a KE has been shown to be essential for the AOP to occur), the experimental data that informs this decision is captured during the evidence evaluation of the KE relationships (e. g. knocking out an upstream KE to evaluate the causal relationship for the pair also informs the essentiality of the KE for the AOP). SR approaches can improve the transparency, consistency, and accuracy of AOP development and include approaches for WoE evaluation and overall assessment of the AOP (Figure  2 ).
The AOP framework provides a structured way of representing mechanistic data that makes it easy for decision makers and stakeholders to review the evidence in support of a public health decision. The use of systematic methods to populate this framework will result in accuracy, consistency, and transparency in the collection and interpretation of the data underlying each AOP. In addition, advances in artificial intelligence (AI) such as natural language processing and machine learning, may add efficiency to the process  (Marshall and Wallace, 2019; O'Connor et al. , 2020; Bozada et al. , 2021). These AI tools are already integral to SR workflows, and include machine assisted topic clustering and prioritization of search results, machine assistance for study screening, normalization of terminology using controlled vocabularies and ontologies, etc.