dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This project is jointly funded through  CSIRO  and the  Earth Systems and Climate Change Hub of the Australian Government 's  National Environmental Science Program. The computation for this work was performed using the  NCI National Facility  at the  Australian National University , and we particularly thank  Dr Ben Evans  and the associated  NCI  staff members for their support. We further thank  Cath Senior ,  David Storkey ,  Richard Hill  and other colleagues at the  Met Office  for helpful discussions during the course of this work, and  Joa ˜o Teixeira  and  Charline Marzin  for model forcing data and HadGEM-GC3. 1 model output fields useful for comparison and benchmarking. We thank  Marcus Thatcher  for his comments on a presubmission draft. Particularly, we thank  Australian Research Council Centre of Excellence for Climate Extremes  for providing the ''harmonised'' code of MOM5 for ACCESS-CM2 implementation.
g. ,  Genersch et al. 2010; Austin 2014; Döke et al. 2015). This is consistent with the science-based recommendations for best beekeeping management practices, suggesting that, among other efforts, attempts to reduce overwintering losses should focus on enhancing colony strength (i. e. , numbers of adult bees and developing eggs, larvae, and pupae, collectively referred to as brood) and food stores (i. e. , nectar, honey, and pollen) in the fall  (Steinhauer et al. 2014). In the present study, we addressed the problem of understanding LSCFS control colony overwintering losses first by analyzing for associations within data from control colonies in 7 LSCFSs. All studies were conducted in North Carolina (USA) between 2013 and 2017. The data consisted of measurements from colony condition assessments (CCAs), in which visual frame coverage was used to estimate numbers of bees in different stages as well as honey and pollen cells.
The CCAs were conducted at prescribed intervals between May (after initial setup of the colonies) and October in the first year of each study, and in March and/or April of the following year to assess overwintering success. Across studies, we compared the different aspects of the LSCFSs with their overwintering outcomes, as well as with their colony conditions at the end of the first year. Multiple factors differed among LSCFSs, not allowing for the identification of clear associations between potential causes and effects from the available data alone. In modeling approaches, all factors of a simulated system can be fully controlled, allowing for analysis of the impacts of isolated factors and their interactions with a system over time. Multiple honey bee colony models have been introduced with the aim of improving our understanding of the interplay of many processes and factors in this complex system  (Becher et al.
A more detailed description of the LSCFSs used for the present study can be found in  Schmolke et al. (2020; this issue). Data from the 7 LSCFSs were used to examine factors associated with control colony overwintering survival or loss. Prior to winter, signs may be discernable that could identify colonies more at risk. The time period of interest in our analysis is therefore the first year of each study, between colony placement in study apiaries (late June or early July) and the end of the honey bee foraging season (late October). Several CCAs were conducted during the first year of each study (Supplemental Data, Figure  S1 ). Measurements from all LSCFSs' control colonies were graphed together to more robustly identify dates at which different metrics may be predictive of overwintering outcome.
infestations were not used due to sparse data availability (only estimated at 2-3 time points/study during the foraging period). Graphing was performed in R software with the package ggplot2 (Wickham 2016; R Core Development Team 2019; LOESS based on weighted least squares at span = 0. 75 with 95% confidence interval based on  Cleveland et al. 1992). Based on these time lines, all colonies' final CCAs prior to winter were further examined to determine whether fall colony condition could serve as a predictor of overwintering outcome. The fall CCAs occurred between 15 and 28 October across the 7 LSCFSs (Supplemental Data, Figure  S1 ). These end-of-season measurements from the 4 colony condition metrics (honey stores, adult bees, pupae, and the ratio of adult bees to honey stores) were graphed, colored by overwintering success.
Statistical analysis of BEEHAVE outputs targeting study design options The goal of our statistical analysis was to identify whether the different feeding schedules and initial colony conditions affected the resulting numbers of adult bees and honey stores in the fall, and if so, to quantify those effects. The scenarios that produced surviving colonies in the fall in all 20 replicates (>0 adult bees, 92% of scenarios) were analyzed separately from the scenarios that produced some or all replicates with dead colonies (0 adult bees, 8% of scenarios) prior to 21 October, due to substantial differences in resulting values. The main analysis used data from the scenarios that produced surviving fall colonies in all replicates. Two linear mixedeffects (LME) models were built, which combined least-squares multiple regression and analysis of variance to account for a grouping structure in the data.
The 8 apiaries, each with known inherent uniqueness, were the random-effect groups statistically represented by intercept shifts. The dependent, or predicted, variable in the first model was the number of adult bees on 21 October simulated with BEEHAVE, and the predicted variable in the second model was the amount of honey in colonies on 21 October in BEEHAVE. The following independent (fixed) variables and interactions were tested in each LME model: the initial number of adult bees and kg of honey in the colony, the amount of sugar/ feeding (as kg of honey equivalent) during treatment and supplemental periods, the start date of supplemental feedings (as number of days after 23 August, the first date applied), and the total amount of sugar in supplemental feedings. Analyses were performed in R software (R Core Development Team 2019) with the package lmerTest  (Kuznetsova et al.
Lidar observations also allow the analysis of aerosol plume height and have been used in several studies to analyze injection height (e. g. ,  Labonne et al. , 2007). However, for this case study, there was no CALIPSO overpass above the fire region so there could be no constraint on the profile at the source of emissions. The classification data are used in Sect. 5. 5. 2 to evaluate the simulated altitude of transport of the biomass burning aerosols downwind from the emissions (Sect. 5. 5. 2). In order to test the uncertainty associated with the various options on burned area processing models, several simulations were conducted, focusing on the largest influences on emissions: without fire emissions, using the emissions BA-FRP and BA-sf with MODIS vegetation, and using the https://doi. org/10.
g. ,  Jackson & Muggeridge, 2000; Jackson et al. , 2009). However, in many subsurface settings, internal facies arrangements and proportions are at best only poorly constrained by sparse wells that provide wireline or core data  (Miall, 1994; Kostic & Aigner, 2007; Musial et al. , 2012). In such cases, databases are commonly employed to provide information about the distributions of facies known from analogous modern rivers and outcropping successions (e. g. ,  Colombera et al. , 2012a Colombera et al. , , 2012b Colombera et al. , , 2013;; Colombera & Mountney, 2019). Such databases are used to inform reservoir models that seek to depict the expected distribution of facies and elements within subsurface successions  (Arnold et al. , 2019). In general, these static reservoir models represent the internal anatomy, lithological configuration and petrophysical properties of hydrocarbon reservoirs as cell-based corner-point reservoir grids  (Farmer, 3 2005; Ringrose & Bentley, 2015).
, 2017)  permit simulation of the distribution of facies inside the reconstructed architectures. The aim of this study is to highlight how two numerical modelling approaches -ChaRMigS  (Parquer et al. , 2017)  and PB-SAND  (Yan et al. , 2017) , can be jointly employed to model the internal architecture of the accumulated deposits of meandering fluvial successions, with consideration of how this architecture varies in relation to the likely evolutionary scenarios of the formative river system. The modelling approach seeks to account for deposition and erosion over time and space as a fundamental control on preserved sedimentary architecture. Specific research objectives are as follows: (i) to compare the contexts of application, the inputs and outputs, and the relative merits and limitations of two modelling approaches used to predict the sedimentary architecture of meandering fluvial channel belts, based on inverse and forward modelling, respectively; (ii) to illustrate how the two modelling approaches can be linked and used sequentially to obtain several fully stochastic realizations of the facies architecture of large fluvial meander-belt successions whose channel fills are imaged in three-dimensional seismic datasets.
, 2020). In PB-SAND, incorporation of abandoned cut-off meander loops is achieved through integration of the outputs of separate modelling steps. It is difficult to incorporate abandoned meander loops specifically in a temporal sequence, because the evolutionary trajectories of the point-bar elements (for example, as sometimes recorded in accretion patterns observed in seismic slices) commonly have been heavily overprinted by more recently developed point-bar elements. The modelling approach of PB-SAND can therefore be used to generate stochastic models of the internal facies architecture of meander belts, but only in cases where the planview accretion geometries of the channel belt can be inferred with confidence, as is sometimes the case with 3D seismic datasets  (Colombera et al. , 2018; Yan et al.
FAKTS database The Fluvial Architecture Knowledge Transfer System (FAKTS;  Colombera et al. , 2012b ) is a relational database storing descriptions of the sedimentary architecture of many modern river systems and ancient fluvial successions, themselves classified on several parameters (e. g. , climate, age, drainage area). FAKTS currently contains data from over 300 sedimentological case studies, and incorporates data derived from both original field studies and the published literature. FAKTS provides a quantitative characterization of sedimentary architectures at multiple scales (e. g. , lithofacies, point bars, channel belts). This database serves to provide input parameters to PB-SAND, relating to the geometry of architectural elements (e. g. , bar and channel-fill dimensions) and to the proportion and geometry of lithofacies (e.
Channel belt of the Oulanka River (Finland) To demonstrate an application of the two modelling approaches considered in this work, ChaRMigS and PB-SAND were applied separately to model the planform evolution of a stretch of the meandering channel belt of the present-day Oulanka River (Finland), which occupies a pre-glacial valley  (Koutaniemi, 1980). The planform evolution can be inferred from a cursory analysis of the valley topography, as seen in a LiDAR acquisition from National Land Survey of Finland and of satellite imagery (Figure  1A ). In addition to the current river path and an abandoned meander loop, the high-resolution topographic dataset allows the identification and digitization of scroll topographies (see the red arrows 1 and 2 and 3 in Figure  1A ). Figure  1  A-LiDAR data of the Oulanka River, Finland.
In the ChaRMigS approach, the point bars observed on the eastern side (red arrow number 3) could only be reproduced by significantly shifting or enlarging the range of the migration offset probability distribution, or by generating an additional abandoned meander bordering the point bars. In parallel to the ChaRMigS application on this dataset, and in order to enable a comparison with ChaRMigS outputs, PB-SAND was used to reproduce the accretion history of the north-western part of the channel belt, on the basis of three manually digitized channel trajectories (corresponding to the current river course and to two earlier river paths inferred from observed scroll geometries). Downstreamtranslating point bars are modelled. This point-bar type is typical of meandering rivers developed in confined valleys where bend expansion is inhibited by confinement by the valley walls  (Smith et al.
Pleistocene succession of the Gulf of Thailand In a second case-study example, ChaRMigS and PB-SAND were again applied separately, in this case to model the planform evolution of a meandering channel belt seen on a time slice from a 3D reflectionseismic survey of a Pleistocene succession from the Gulf of Thailand  (Reijenstein, et al. , 2011). FAKTS was used to provide input parameters on channel-bar scaling relationships and on the proportions of lithologies, for modelling the geometry and facies distributions of point-bar and channel-fill deposits. The seismic data clearly reveals the planform bar geometry of the channel belt, within which the path of the last abandoned (avulsed) channel can be recognized, together with abandoned meanders that have undergone cut-offs. The internal accretion geometry of the point bars can be mapped to infer the paths taken through time by the river channel.
An overall fining-upward facies arrangement -as is common for the deposits of these types of meandering fluvial systems  (Nanson, 1980)  -is used to depict the internal facies distribution within the modelled lateral-accretion packages, as shown in cross sections (Figure  4C ). Contrasting facies proportions have been selected for deposits of point-bars (gravel: 10%, coarse sand: 20%, medium sand: 40%, fine sand: 10%, silt: 10%, clay-rich mud: 10%) and counter-point-bars (gravel: 10%, coarse sand: 10%, medium sand: 10%, fine sand: 20%, silt: 40%, clay-rich mud: 10%). These values of facies proportions are taken from the FAKTS database of geological analogues and are representative of meander belts of this scale  (Colombera et al. , 2013). The proportion for each facies varies systematically between -5 % to +5%, to mimic inherent natural variability  (Yan et al.
Considerations on the modelling approaches Based on the separate application of the two modelling techniques to the two datasets presented above, the weaknesses and strengths of the two approaches can be discussed. The case-study example presented above highlights how PB-SAND is able to model meander-belt sedimentary architectures in 3D using input channel trajectories that can be inferred from clearly identifiable point-bar accretion geometries, in situations where these geometries can be seen in 3D seismic surveys. However, integration of abandoned meanders cannot be achieved directly in PB-SAND, and the trajectories of these meander loops cannot be used to condition the model outputs. This can, however, be achieved using ChaRMigS, which produces a range of possible planform geometries associated with equiprobable scenarios of channel evolution while being conditioned to abandoned meander loops.
Workflow and dataset The ChaRMigS and PB-SAND modelling approaches each have advantages and limitations. This naturally leads us to propose a combined use of both methods following the workflow illustrated in Figure  5. The main idea of this workflow is to generate scenarios of palaeo-channel evolution from incomplete observations on a seismic slice using ChaRMigS, then to use the channel trajectories in PB-SAND to generate facies models, using parameters gathered from the FAKTS database. We employed this approach to generate 3D models of the internal facies architecture of meander-belt deposits imaged in seismic data from the Pleistocene of the Gulf of Thailand. In this work, sets of channel trajectories representing stochastic outputs of ChaRMigS have been smoothed, trimmed and loaded as input into PB-SAND, together with analogue data relating to the geometry of element and facies proportions from the FAKTS database (see below).
The proposed workflow is applied to show how the ChaRMigS and PB-SAND modelling approaches, augmented and supported by information from the FAKTS database, can be used to reveal complementary insights into the evolution of fluvial systems and their preserved meander-belt architectures. This case study was chosen to demonstrate how the two modelling approaches can be used in combination to provide a novel workflow for subsurface modelling (Figure  5 ). Such an approach is especially useful for situations in which channel belts of meandering fluvial systems (including associated channel fills) are recognizable in 3D seismic surveys, but where internal point-bar accretion geometries cannot be discerned. In this case, ChaRMigS is applied to generate multiple stochastic realizations of the internal accretion history of a meander belt in a way that is compatible with the observable geometries of abandoned channel fills seen on a seismic time or stratal slice.
The accretion histories and styles predicted using ChaRMigS are maintained overall, however. This is recognizable when comparing, for example, the reconstruction of the lateral migration of the abandoned meander 2 in Figure  7E  and the downstream migration of abandoned meander 1 in Figure  8E. The two planform realizations produced by PB-SAND (Figures  7  and 8 ) have been used as frameworks for modelling cross-sectional geometries and facies distributions. The internal facies architecture of the modelled point-bar elements has been simulated using data on the proportions of facies in a range of successions that act as geological analogues contained in the FAKTS database  (Colombera et al. , 2012b (Colombera et al. , , 2013)) , as presented in Figure  9. The proportions of each facies (fines, sand, and gravel) are randomly sampled between the first and third quartile in such way that they add up to 100 %, using the approach adopted in  Yan et al.
Abandoned-channel fills are modelled as homogeneous mud plugs. The thickness of the channel fills and of the associated point-bar deposits reflect scaling relationships between channel-fill width -which is observed in the seismic data -and thickness, as based on geological analogues from the FAKTS database. For the sake of simplicity, temporal variations in river depth that match observed changes in channel-fill width have not been modelled in this case, although such functionality is incorporated in PB-SAND. The facies architecture of the modelled meander belt is presented on cross-sections, which have been traced through both realizations in Figure  7E  and Figure  8E  at corresponding positions (Figure  10  and    11 ). Green arrows point to contacts between accretion packages in point-bar units with contrasting accretion style and lithological make-up.
Reservoir-modelling applications Seismic data are routinely used for constraining subsurface reservoir models. For example, seismic attributes are employed as soft-data (e. g. , facies probability) for conditioning geostatistical models (e. g. ,  Strebelle, 2002; Yao, 2002; Rongier et al. , 2017a) , or can be used to determine outputs of process-based forward models that best honour subsurface observations  (Koneshloo et al. , 2017). However, geological insight that can be drawn from geometries of sedimentary units seen in 3D seismic datasets is typically underused in reservoir-modelling practice. The presented workflow leverages on planview observations of meander-belt architectural elements, i. e. point bars and channel fills, to build stochastic models of facies heterogeneity that honour the distribution of these elements, through the sequential application of two algorithms: ChaRMigS and PB-SAND.
, 2019). A second general avenue for further research is to better and more effectively honour features seen on seismic sections and stratal or time slices or other types of subsurface data. One way of achieving this more effectively is by finding ways to infer model parameters to generate more models that automatically honour all observations. In the present state of the methods, some model results need to be eliminated by experts. For example, when applying ChaRMigS on the Pleistocene Gulf of Thailand data set, we observed that several realizations could not incorporate all abandoned loops. This may occur in particular when the channel path crosses an abandoned meander, as it may then need to migrate for a large number of time steps before the integration can be considered. In a sense, this represents a "missed opportunity" for meander loop integration, which can hardly be recovered as the interpreted older loop should then no longer be visible.
Conclusions This work has presented applications of inverse (ChaRMigS) and forward (PB-SAND) models for the architecture of fluvial meander belts; these models have been applied to modern and ancient fluvial systems for scopes of comparison and to demonstrate how they can be linked sequentially in a new workflow for geocellular reservoir modelling. Based on input trajectories of the planform of channel fills, including those of oxbow-shaped abandoned meander loops, ChaRMigS generates several equiprobable migration patterns that can be used to assess uncertainty about internal reservoir architectures. PB-SAND instead uses input channel trajectories, such as those produced by ChaRMigS, to generate a geometric framework of meander-belt accretion and to populate this framework by facies of different types, stochastically, based on suitable analogue data, for example as provided by FAKTS, a database of real-world case-study examples.
When working with a 3D seismic dataset in which abandoned meander scars and avulsed channel fills are observed, but in which internal point-bar growth geometries are below seismic resolution, the proposed workflow, which combines ChaRMigS with PB-SAND, makes it possible to explore a range of meander-belt facies architectures that are realistic with respect to expected styles of accretion and associated lithological heterogeneity. This novel approach is valuable for quantification of subsurface uncertainty.
Figure4PB-SAND modelling results of the seismic image from the Gulf of Thailand (Reijenstein et al. , 2011)  presented in Figure3A. A-Input lines for PB-SAND reconstruction. B-Modelled accretion surfaces between two consecutive input trajectories are denoted from blue to red in a temporal sequence. C-Cross-sections of the reconstruction shown in B. The reconstruction here is based on four channel-path centrelines. Then, a simulation of the internal facies architecture of the point-bar deposits has been undertaken to reveal the expected facies distribution within the reconstructed system. The cross sections depict the expected spatial distribution of six different facies within the point-bar elements. Facies distributions are determined from distributions from known natural point-bar examples stored in the FAKTS database.
Acknowledgements The authors thank  Hernan Reijenstein  for authorizing the use of seismic data from Reijenstein et al. (2011). MP was supported by an award from the  International Association of Sedimentologists (IAS). NY, LC and NPM acknowledge sponsors of the  Fluvial & Eolian Research Group (Aker BP, Anadarko ,  Areva  [now Orano],  BHP Billiton ,  Cairn India [Vedanta], Chevron ,  ConocoPhillips ,  Equinor ,  Murphy Oil ,  Nexen-CNOOC, Occidental ,  Saudi Aramco ,  Shell ,  Tullow Oil ,  Woodside , and YPF) and our partner Petrotechnical Data Systems (PDS) for financial support. LC has been supported by the  Natural Environment Research Council (Catalyst Fund  award  NE/M007324/1 ,  Follow-on Fund   NE/N017218/1 ). PC, GC and MP acknowledge the support of the RING Consortium managed by  ASGA (ANDRA, CCR ,  Chevron ,  Equinor ,  ExxonMobil ,  IFPEN ,  Emerson ,  Petrobras ,  PDS ,  Repsol ,  Saudi Aramco, Schlumberger, Total, WintershallDEA  and the associate.