dataesr/scientific-paragraphs-categorization huggingface dataset:
Exposure Exposure to the ItFits-toolkit will serve as a measure to determine if the measured change in outcomes can reliably be related to the use of the toolkit. Usage data will be automatically collected by system logs recording time stamped starting and stopping of ItFitsmodules (use) and a binary confirmation of the existence of output of the modules (result). Satisfaction As the ItFits-toolkit is a newly developed tool, satisfaction of use will be assessed to establish to what extent the toolkit is able to fulfil implementers' needs and expectations in tailored implementation. Satisfaction will be assessed with the short version of the Client Satisfaction Questionnaire (CSQ-3)  [45] , which has good psychometric properties and has been tested in numerous studies and diverse samples  [46, 47].
Data collection Data will be collected on implementation sites and staff level through (1) a central data management system specifically built for the purpose of the study and (2) eventbased log files of the ItFits-toolkit. Online surveys will automatically be sent to participants via email with the request to fill in a specified measure. The measurement time points are pre-scheduled for 3-month intervals during the 30-month study period. Data on demographics are collected once, when a participant enters the study. Degree of normalization, uptake, implementation costs, and organizational readiness for change will be measured at 3-monthly intervals. Exposure data will be collected continuously, and data on satisfaction and usability will be recorded at the end of the exposure period (see Table  2 ).
Consequently, trial data will be analysed using generalized linear mixed modelling (GLMM)  [55]  with service uptake as the dependent variable, and measurement wave (time), ItFits-toolkit use (yes/ no), and interaction between time and ItFits-toolkit use as independent variables. To account for the expected intervention lag effect, a fractional term for the 'ItFits' parameter will be included in the 6-month minimal exposure time (ranging from 0 to 1, i. e. 0-½-1). Service uptake outcomes will be modelled in a two-level GLMM, since these measures are collected at the site level only. Thus, to account for the correlation structure of the uptake outcome, 'Wave' is modelled to be clustered at the 'Site' level. All regression parameters will be allowed to vary. For exploratory purposes, measures of exposure to the ItFits-toolkit (event-based log files showing intensity of use and level of continuous use), CSQ, SUS, and ORIC questionnaire data will be added as additional predictors of outcome in the above-described regression models.
The conceptual ideas within these bodies of literature will enable the research team to focus on the work through which the ItFits-toolkit is implemented, what motivates individuals to work with the ItFits-toolkit, and how the ItFits-toolkit facilitates the structuring of time, resources, and people to support implementation. Four qualitative research methods will be used within the process evaluation, including theoryinformed interviews with Implementation Leads and main informants (e. g. implementation practitioners and trainers of the ItFits-toolkit), in situ and distal observations of implementers engaged with the ItFits-toolkit, process data from the use of the ItFits-toolkit, and analysis of documents, texts, and technological specifications produced and made available by ItFits-toolkit users.
Trial status The ImpleMentAll study runs from March 2018 to March 2021. Implementation sites across Europe and Australia were recruited following a purposeful sampling approach. According to the closed cohort design of the study, recruitment of clusters (implementation sites) was completed prior to the first measurement wave (before September 2018). Subsequently, the included implementation sites engaged in recruiting a minimum number of staff participants (n = 19 per cluster) to ensure a stable and sufficient sample for repeated data collection during the trial. Staff participant recruitment is open and continues throughout the trial to allow for replacements of potential study drop-outs. Implementation sites have localized the study protocol, translated measurement instruments and obtained ethical approval.
Acknowledgements The authors want to express their gratitude to the funding bodies, the  European Union  by means of the  European Commission 's  Horizon 2020 research programme  and the  Australian government , for being provided with the financial capacity to conduct the ImpleMentAll study. We thank the  ImpleMentAll consortium  for every individual contribution and team effort and for bringing together the required expertise, experience, and organizational capacity to make this study a success. Specifically, we thank the local research and implementation teams, the internal Scientific Steering Committee, and the  External Advisory Board  for their input in designing the ImpleMentAll study. We remember  Dr. Jeroen Ruwaard. His unexpected death ( 16 July 2019 ) leaves behind an inexpressible void, to his family, his friends, close colleagues, the  ImpleMentAll consortium , and the wider scientific and mental health community.
Model The integral model was composed of the 18 O-enabled soil-vegetation-atmosphere transfer model MuSICA  (Ogée et al. , 2003 (Ogée et al. , , 2009;; Wingate et al. , 2010; Gangi et al. , 2015) , parameterized as in  Hirl et al. (2019) , and a new allocation-and-growth model (Fig. 1 ). MuSICA is a multilayer multileaf model that simulates CO 2 , water and energy redistribution and isotopic exchange processes in an ecosystem based on current mechanistic understanding (see Supporting Information Methods S1). MuSICA requires half-hourly climate data at a reference level above the vegetation (0. 5 m here) as well as the isotopic composition of rainfall, CO 2 and water vapour at the same reference level. Meteorological variables were obtained from the meteorological station at Munich airport at about 3 km from the study site (wind speed, precipitation, air temperature, RH, air pressure), from two other meteorological stations at 10 and 12 km distance (radiation), and from an eddy flux station installed at the experimental site (CO 2 concentration)  (Hirl et al.
(2017)  on tree species from different functional groups growing across a range of temperate to cold environments. Partial evidence in favour of a temperature-sensitive ϵ bio was presented by both groups, although overall evidence was inconclusive because of uncertainties in either the models or the climate and site data. Strong evidence for a temperature-sensitive ϵ bio is reinforced in our comprehensive model parameterization and validation study against an extensive field dataset. In particular, the leaf water modelling, partitioning of source (δ 18 O source )   and enrichment effects (Δ 18 O leaf ) on leaf water isotope composition (δ 18 O leaf )  (Hirl et al. , 2019)  permitted a very robust modelling of Δ 18 O cellulose that could be contrasted with observations of Δ 18 O cellulose , across a multiyear dataset using the same set of model parameters.
Our results have important implications for the interpretation of seasonal, interannual and geographical variations of Δ 18 O cellulose and δ 18 O cellulose in terms of plant physiology and climate. Existing datasets, for example on tree-ring Δ 18 O cellulose or δ 18 O cellulose from boreal to subtropical ecosystems, should benefit from a re-evaluation that considers the varying nature of biochemical fractionation and isotopic exchange with source and leaf water. In addition, there is a great need for more targeted studies investigating the mechanisms underlying the large observed effect of temperature on ϵ bio and the RH-dependent variation of p ex p x. Such knowledge will help us to better understand the physiological information that is imprinted in cellulose-18 O signals from past and present environments.
Cabrera et al. , 2020) (2011)e air humidity (RH) and p ex p x from controlled environment chamber experiments with Lolium perenne (closed and open squares: Lehmann et al. , 2017; green points: J. C. Baca Cabreraet al. unpublished data). For both datasets, p ex p x was calculated based on Eqn 1, using Δ 18 O leaf and Δ 18 O cellulose data. The biochemical fractionation (ϵ bio ) was computed from the temperature (T) of the chamber air according to the relationship found bySternberg & Ellsworth (2011)for submerged aquatic plants (ϵ bio = 0. 0073T 2 -0. 4375T + 32. 528; see their Fig. 2) and yielded ϵ bio = 26. 8‰for the unpublished data of JCBC, RTH, JZ and HS (green points) and ϵ bio = 25. 5‰ for the study ofLehmann et al. (2017)(closed squares). Open squares represent p ex p x calculated by assuming ϵ bio = 27‰ (as reported byLehmann et al.
Acknowledgements We thank  Erna Eschenbach †, Angela Ernst-Schwärzli ,  Anja Schmidt ,  Monika Michler ,  Hans Vogl ,  Richard Wenzel  and  Lenka Plavcová  for technical assistance, and  Iris Köhler  for previous discussion. JZ was supported by the  China Scholarship Council. We are grateful to  Xin Song  and two anonymous reviewers for constructive comments. This work was supported by the  Deutsche Forschungsgemeinschaft  ( DFG SCHN 557/9-1 ).
g. ,  Patra et al. , 2018 Patra et al. , , 2020;; Bevilacqua et al. , 2019b). Model uncertainty is probably the most difficult class of epistemic uncertainty to evaluate robustly, but it is indeed a potentially large component of the total uncertainty affecting PDC inundation forecasts. In this paper, we test the suitability of box-model approach (described in section 2. 1 and in Appendix A), as implemented numerically in the PyBox code  (Biagioli et al. , 2019) , by quantifying its performance when reproducing some key features of the remarkably well-known PDC deposits from one of the best studied and documented volcanic events, the AD 79 eruption of the Somma-Vesuvius (SV) volcano (detailed in section 2. 2). It is indeed accepted that the box model is able to describe the main features of large-volume (VEI 6 to 8;  Newhall and Self, 1982) , low aspect ratio ignimbrites, whose dynamics is dominantly inertial  (Dade and Huppert, 1996).
, 2009; Kelfoun, 2011; Ogburn and Calder, 2017; Aspinall et al. , 2019). PDCs are driven by their density excess with respect to the surrounding air: the density of the current ρ c is defined as the sum of the density of an interstitial gas, ρ g , and the bulk densities of the pyroclasts carried by the flow, (ρ s i ) i=1,…,N. In this study we assume ρ atm ≠ ρ g , i. e. the interstitial gas is hotter than surrounding atmosphere, differently from  Neri et al. (2015)  and  Bevilacqua et al. (2017). The code allows ρ atm > ρ g , but thermal properties remain constant for the duration of the flow, and in this study we assumed ρ atm = ρ g. The thermodynamics of cooling effects are explored in  Bursik and Woods (1996). A proper way to express the density contrast between the current and the ambient fluid is given by the reduced gravity  ′ , that can be rewritten in terms of the densities and the volume fractions described above (see  Biagioli et al.
Comparison between field data and simulation outputs Since the PyBox code assumes axisymmetric conditions, the thickness outputs are equal along all the radial directions of the collapse, and only vary as a function of the distance to the source. These output data were compared with the mean radial profiles of unit thickness (for both EU3pf and EU4b/c) as derived from the digital models of deposit in  Cioni et al. (2020). For building the radial profiles, the average thickness was estimated over concentric circles drawn with a 100-m step of distance. The radial thickness profiles were drawn starting from a distance of 3 km from the vent, as no thickness data are available for sites closer than 3 km. Due to the lack of reliable data, we have moreover excluded from our analyses the portions of the circles located in marine areas.
General considerations Testing PyBox with respect to field data is aimed at two main objectives: i) quantifying the degree of reproduction of the real PDC deposit of Plinian eruptions in terms of thickness, inundation area and grain size and ii) evaluating the reliability of the code when considering different assumptions, i. e. polydisperse vs. monodisperse situations, and 360° axisymmetric conditions vs dividing circular sectors. Before commenting our results, two main general considerations, in common for both EU3pf and EU4b/c, deserve a special discussion.
Runout truncation and non-deposited material We remind that PyBox code produces the map of the inundated area  (Neri et al. , 2015; Bevilacqua, 2016) , by truncating the runout wherever the kinetic energy of the flow is lower than the potential energy associated to a topographic obstacle (Section 2. 1 and Appendix A). In this way, however, the material that lies beyond the truncation is neither redistributed nor considered any more. However, depending on the topography in our case study, this amount of material is not extremely high. For instance, EU4_poly_AS (Table  4 ), in its SE part, has several truncations due to the intersection of the decay function of kinetic energy with several topographic barriers, i. e. the Apennines to the ENE and the Sorrentina Peninsula to the SE (Figs. 2  and 7 ).
For the whole SE part of the deposit, the topographic barriers are located between 11. 85 km and 19. 25 km from vent area, with a mean vale of 15 km. If we truncate PyBox deposit in correspondence of these three limits, the non-deposited volume is between 3. 46x10 6 m 3 (cut at 19. 25 km) and 2. 3x10 7 m 3 (cut at 11. 85 km), with a mean value of 1. 27x10 7 m 3 (cut at 15 km). Considering that the volume collapsed to the SE is 1. 5x10 8 m 3 , the non-deposited volume corresponds therefore to a value between 2% and 15%, with a mean of 8%. The amount of volume effectively "lost" is relatively small, also considering that the total volume of the collapsing mixture is inclusive of the EU4c unit (co-ignimbritic part). However, further development of the code might consider a strategy to redistribute this non-deposited material (e.
Conclusions We have evaluated the suitability of the box-model approach implemented in the PyBox code to reproduce the deposits of EU3pf and EU4b/c, two well-studied PDC units from different phases of the AD 79 Pompeii eruption of Somma-Vesuvius (Italy). The total volume, the TGSD, the grain densities, and the temperature obtained from the field data are used as the main inputs of PyBox. The model produces several outputs that can be directly compared with the inundation areas and radially-averaged PDC deposit features, namely the unit thickness profile and the grain size distribution as a function of the radial distance to the source. We have performed simulations either under polydisperse or monodisperse conditions, given by, respectively, the total grain size distribution and the mean Sauter diameter of the deposit.
Acknowledgements This work has been partially supported by: project  V1 "Stima della pericolosità vulcanica in termini probabilistici " funded by  Dipartimento della Protezione Civile (Italy) , project  EUROVOLC  " European Network of Observatories and Research Infrastructures for Volcanology " funded by  EU commission , project  FISR2017  " Sale Operative Integrate e Reti di monitoraggio del futuro : l'INGV 2. 0" funded by the  Ministry of University and Research, (Italy) ,  IDEX-ISITE  initiative  16-IDEX-0001  ( CAP 20-25 ) funded by the  French government , and  ClerVolc  project  -Programme 1 "Detection and characterization of volcanic plumes and ash clouds " funded by the  French government 'Laboratory of Excellence' initiative. The manuscript does not necessarily represent the official views and policies of the  Dipartimento della Protezione Civile -Presidenza del Consiglio dei Ministri.
This study investigates how typological and metaphorical construal differences may affect the use and frequency of temporal expressions in English and Spanish. More precisely, we explore whether there are any differences between English, a satelliteframed language, and Spanish, a verb-framed language, in the use of certain temporal linguistic expressions that include a spatial, deictic component (Deictic Time), a purely temporal relation between two events (Sequential Time) or the expression of the duration of an event (Duration). To achieve this, we perform two different types of studies. First, we conduct an informational gain or loss analysis of 1,650 of Englishto-Spanish translations extracted from parallel corpora. Secondly, we compare the frequency of 33 English and 27 Spanish temporal expressions in two similar written online corpora (EnTenTen and EsTenTen, respectively) and a television news spoken corpus (NewsScape).
(8) This case was handled perfectly from beginning to end. It's unfortunate that the prosecuting. (AlJazeera, AlJazeera News, 23-10-2013, NewsScape Library). However, temporal duration can also be expressed through a different use of spatial metaphors. Languages such as Spanish and Greek have been reported to refer to temporal duration by means of size or quantity metaphors  (Casasanto et al. , 2004; Casasanto, 2005 Casasanto, , 2008 Casasanto, , 2010;; Dolscheid et al. , 2013; Baksteen, 2016). The TIME IS QUANTITY metaphor has been examined by just a handful of psycholinguistic studies  (Casasanto, 2010; Baksteen, 2016; Bylund and Athanasopoulos, 2017) , and it is often neglected by time conceptualization scholars, who focus on the more common TIME IS SPACE construal in English. When referring to temporal duration in terms of quantity, we no longer talk about duration in terms of the separation between two points in time (1-dimensional space), but rather as a unit located in a 3-dimensional space  (Dolscheid et al.
METHODOLOGY Materials This study employs two different corpus software programs as well as two corpora for performing the English linguistic searches. The first program used is Sketch Engine, a specialized tool for corpus research  (Kilgarriff et al. , 2014)  which allows highly detailed corpus linguistic searches. Through this tool we have access to the English Web Corpus 2015 (EnTenTen), a corpus of 15 billion words derived from internet text. The Sketch Engine software ensures the validity of this corpus by applying deduplication tools to remove duplicated content found on the internet (including those copies that are slightly adapted from webpage to webpage). This corpus also applies the JusText tool to remove unwanted textual content which includes texts with little linguistic value, such as texts made up of incomplete sentences, advertisements, navigation menus and text snippets.
The second corpus tool we employ is CQPWeb  (Hardie, 2012) , a free, more flexible corpus program adapted by Peter Uhrig  (Uhrig, 2018)  to store the textual data from the NewsScape Television archive, a digital collection of more than 350,000 television news programs, with more than 250,000 hours of television with their associated subtitles. The result is a textual database of more than 2 billion words coming from subtitled television programs in English that have been recorded since 2004 FIGURE 2 | Typology of temporal events selected for our study. until the present day, including channels such as CNBC, KABC, BBC, and FoxNews. This database includes television news and all types of talk shows, excluding series, films and other highly scripted television content. The searches in Spanish were performed with the same two corpus software programs as well as the Spanish equivalents of the two English corpora.
Through Sketch Engine, we used the EsTenTen2018 corpus, which belongs to the TenTen group of internet texts compiled through Sketch Engine. This Spanish web corpus contains over 17. 5 billion words, including European (49%) and American (46%) Spanish, the remaining 5% being unidentified Spanish. It applies similar deduplication tools to EnTenTen 2015, as well as the JusText software to avoid the inclusion of non-relevant linguistic items. The second corpus we use is the Spanish version of the CQPWeb NewsScape Television archive corpus. Even though the content and structure of this corpus is similar to the English version, also containing television news and talk shows in both European and American Spanish, the size of the corpus is smaller (78 million words) in comparison to the English equivalent (2 billion words).
The last tool we used for this study is the online dictionary Glosbe, 5  which allows users access to both open-source translation memories and free databases of parallel texts. The translation memories are obtained from published free parallel corpora, such as MultiUn, UN-2, EurLex-2, Europarl8, and Opensubtitles2011. Currently, the online platform offers parallel translation from English to Spanish of more than 98 million sentences. This software displays parallel translations of the original and the translated texts of the languages chosen by the researchers, allowing for the direct comparison of both excerpts.
Deictic Expressions With Directional Language (DDL) As stated in the methodology section, we searched for the two most frequent expressions for temporal deictic directional expressions in English and noted down the translations found in the parallel corpus Glosbe. The first one was (UTime) ahead. The whole list of translations is available in Appendix 3; the most common ones are listed in Table  1. We also looked for the phrase back then, which is the most frequent one as stated in our methodology section. There were many possibilities for translation (Appendix 3); the most common can be seen in Table  2. Summarizing both tables, we see that in a vast majority of cases (more than 90%), some information is lost when translating English deictic expressions with directional language into Spanish.
Corpus Analysis Deictic Expressions With Directional Language (DDL) In the case of English, the most frequent expression was the construction (UTime) ahead, which turned out to be more frequent overall in the EnTenTen corpus than in NewsScape (but was also the most frequent one in this oral corpus). The second most frequent was back then, with a sharp drop in the rest of options [(UTime) behind/in front of ]. Combined, all these expressions rendered per-million-word (PMW) frequencies of 13. 17 in EnTenTen and 31. 5 in NewsScape (suggesting that this type of expression could be more natural and frequent in oral speech). In contrast, the Spanish searches were much lower in both corpora: 0. 60 PMW in EsTenTen, and 1. 75 PMW in NewsScape. The full results are listed in Table  5.
Deictic Expressions With Non-directional Language (DnDL) In this category, the most frequent expressions in English was near future, with a frequency of 9. 14 PMW in EnTenTen and 2. 52 in NewsScape. The second and third most frequent expressions were distant future (0. 69 PMW in EnTenTen and 0. 38 in NewsScape) and distant past (0. 47 PMW in EnTenTen and 0. 12 in NewsScape; see Table  6 ). In Spanish, the most frequent equivalent terms were futuro próximo and futuro cercano, with a combined frequency of 3. 37 PMW in EsTenTen and 1. 82 PMW in NewsScape, followed by pasado reciente (0. 84 PMW in EsTenTen and 0. 21 PMW in NewsScape), showing again a lower frequency than in their English counterparts. It should be noted that in both cases, there is a lower frequency in the oral corpus.