dataesr/scientific-paragraphs-categorization huggingface dataset:
Optimized basal sliding coefficients and steady-state modeled geometry for the Antarctic ice sheet were obtained after a forward-in-time integration of 60 000 yr with thermomechanical coupling. The initial temperature field (prior to that forward run) is derived from a steady-state temperature solution based on the present-day surface temperature, surface mass balance and geothermal heat flux. The latter is based on the seismic-based dataset due to  Shapiro and Ritzwoller (2004). For the relaxation, ice shelves are allowed to evolve and the hybrid shallow-shelf-shallow-ice approximation (HySSA;  Bueler and Brown, 2009)   gain the relaxed geometry (see Fig. A8 ), representing the initial geometry for the PARASO configuration. The duration of 20 years is the result of a trade-off between gaining quasi-steady-state ice-flow velocities and reducing the iceshelf thinning during the relaxation.
The snow liquid water and ice content for layer i = 1 to 5 is set to w liq,i = 0 kg m -2 and w ice,i = z i ρ sno kg m -2 , respectively. Although a 1 m SWE snowpack is too small to fully represent the firn layer, which can extend to more than 100 m in Antarctica (van den  Broeke et al. , 2009) ,  Souverijns et al. (2019)  showed that this choice can lead to a decent SMB representation in CCLM 2 as long as the adaptations from van  Kampenhout et al. (2017)  are included in the CLM snow module (except for the snowpack depth). For future PARASO-related work, keeping a maximum snow depth of 1 m is also convenient to limit the spinup phase of the snowpack to a few years at most. Multiple surface datasets are also required to initialize CLM. They are listed in Table  2. 5 of  Oleson et al. (2013)  and come from a variety of sources.
Depending on the domain (location, resolution), datasets can be downloaded and regridded on a case-by-case basis using the CESM tools (the procedure is described in chap. 1 of  Vertenstein et al. , 2013). Note that one specificity of PARASO lies in the modifications of the land mask and mean elevation, carried out to be consistent with the f. ETISh relaxation run (see Fig. 5 ). In particular, the ice-shelf surface elevation is represented, while the original CESM data simply assume all ice shelves to be exactly at sea level.
(2020). These results support that the geometry is a main driver of the observed melt biases. The importance of cavity geometry on simulated melt rates has already been emphasized by previous studies  (Schodlok et al. , 2012; Rosier et al. , 2018; Goldberg et al. , 2019; Brisbourne et al. , 2020; Goldberg et al. , 2020; Wei et al. , 2020). Figure  7  compares PARASO and the PARCRYO experiments, corresponding uncoupled stand-alone f. ETISh simulations for different forcings (see Table  4 ). Overall, PARCRYO and PARASO display similar behavior across most coastal regions, and in particular the Antarctic Peninsula, with a moderate grounded ice-thickness increase up to a few meters (Fig. 7b-c ). That thickening also occurs for PARCRYO SMB (Fig. 7d ) and agrees well with the differences between the forcing SMB datasets (Fig.
PARCRYO and PARCRYO SMB , which are forced by the identical SMB dataset (from PARATMO), show a very similar evolution of the grounded ice (Fig. 7e ), while the change of the volume above floatation (VAF) for PARASO clearly follows the enhanced SMB. A more detailed discussion of the differences between AEROCLOUD, PARATMO and PARASO can be found in Sect. 5. 3. Other ice-thickness changes occur at the grounding line (Fig. 7a-c ), as the relatively coarse resolution and numerical uncertainties of the grounding-line flux induce small oscillations between neighboring grid cells in the grounding-line position. Furthermore, the experiments show a thickening of many narrow ice streams (e. g. , Pine Island Glacier, Byrd Glacier, Rutford Ice Stream), and only Thwaites Glacier shows significant grounded ice mass loss (Fig.
Besides the coupling itself, the PARASO biases may be imputable to changes in each model's configuration that led to apply each component in conditions different from the ones in which they were developed and calibrated. Yet, our choice has been to retain these changes, since they are relevant. For example, taking the Antarctic surface topography from f. ETISh as initial CCLM 2 geometry instead of the standard dataset made the PARASO setup more consistent intermodel-wise. However, CCLM 2 parameters had been tuned to this standard topographical dataset, and this might have impacted the results. In other words, we have preferred fundamental consistency over the precise agreement with current observations. Incorporating our novelties while limiting biases also identifies clear paths for model improvements.
Below, we provide three potential bias sources and perspectives related to their corrections or limitations. We also refer to Appendix E for more details on tuning experiments which have led to PARASO, as the "out-of-the-box" coupling between PARASO components led to even stronger biases. First, in the vicinity of their common interface, the ocean and atmosphere are highly sensitive to their boundary conditions  (Miller et al. , 1992; Large et al. , 1997; Torres et al. , 2019a, b). For PARASO, the interface boundary condition has been altered on both sides for enforcing COSMO-NEMO intermodel compatibility. Besides being assessed from a dynamical model instead of an external dataset (such as a reanalysis), ocean-atmosphere fluxes are computed differently in PARASO compared to either NEMO or COSMO stand-alone experiments.
Performing COSMO stand-alone experiments with tiled fluxes has not been pursued because this would have required further adapting the COSMO sources and forcing datasets. In a recent study,  Heinemann et al. (2021)  present a nonlinear tiling approach for COSMO (similar to that of PARASO) as an enhancement of a non-conserving method  (Gutjahr et al. , 2016) , which also relies on sea-ice thickness (not considered in PARASO). This may be of use for future studies intending at investigating the sole impact of non-binary tiling. Alternative approaches are possible to locally reduce the biases in the air-sea fluxes, in particular by nudging them to reanalysis data, as done for instance in Ho-Hagemann et al. (  2020 ) in a recent study coupling NEMO and COSMO. This has the advantage of providing model results closer to observations but at the cost of perturbing the atmosphere-ocean feedbacks and adding artificial energy sources and sinks at the surface, thus limiting the applications of the model outside of presentday conditions.
Third, another potentially large source of biases is the updated Antarctic continent geometry used in PARASO. As described in Sect. 4. 3, the initial Antarctic topography is derived from a f. ETISh relaxation run, which displays clear differences from observations, related to this model's specific biases and to the forcing datasets it relied on (SMB from COSMO, ice-shelf melt rates from NEMO). Out of intermodel consistency, all PARASO initial geometries have been directly derived from this f. ETISh relaxation run. In the atmosphere, the signature of the new topography can be seen on the differences between AEROCLOUD and PARASO (see Fig. 5  and Sect. 5. 3). In the ocean, the near-Antarctic bathymetry is crucial to the warm water intrusion into the cavities, and thus to the ice-shelf melt rates (see Goldberg et al.
Accordingly, we did not impose any relaxation in the inner domain of the atmospheric model towards the large-scale driving model (ERA5). In this way, the CCLM 2 dynamics is preserved. PARASO uses the default snow roughness length of CLM4. 5 of 2. 4 × 10 -3 m, which is constant in time and space. This contrasts with AEROCLOUD, for which the https://doi. org/10. 5194/gmd-15-553-2022 Geosci. Model Dev. , 15, 553-594, 2022 snow roughness length had been set to 10 -5 m. Moreover, PARASO uses the standard snow and ice module of CLM4. 5  (Oleson et al. , 2013)  and does not include the modified scheme implemented in AEROCLOUD. Future sensitivity studies of the modified snow and ice scheme and of the value for the surface roughness length are planned. In COSMO, the turbulence scheme uses a 1-D closure with a prognostic equation for TKE.
Finally, sea-ice dynamical properties were also slightly retuned to limit sea-ice compaction related to the relatively strong PARASO winds. One signature of this phenomenon is the relatively small bias in sea-ice volume (see Fig. 8b ) compared to extent: the PARASO sea-ice pack is small in area but significant in volume; hence, it is quite thick (see Fig. A7 ). While the increase of these sea-ice dynamical parameters is quite significant (+50 %), the uncertainty of their actual value is also quite high  (Tsamados et al. , 2014; Ungermann et al. , 2017). Code and data availability. The full PARASO sources are available to CLM-Community members on their RedC (https://redc. clm-community. eu/ → COSMO-CLM → "Downloads"). In addition to this tarball, all PARASO sources, at the exception of their COSMO parts, are publicly available for didactic purposes at https://doi.
org/10. 5281/zenodo. 5576201 (Pelletier et al. , 2021a). All the input data for running a 3-month long PARASO experiment are publicly available at https://doi. org/10. 5281/zenodo. 5588468 and https://doi. org/10. 5281/zenodo. 5590053 (Pelletier et al. , 2021b; Pelletier and Helsen, 2021). Scripts and data for generating all figures contained herein are publicly available at https://doi. org/10. 5281/zenodo. 5763819 (Pelletier, 2021). The COSMO-CLM model is free of charge for all research applications; however, access is license restricted (see http://www. cosmo-model. org/content/consortium/licencing. htm, last access: 24 August 2021). To download, the user needs to become a member of the CLM-Community or the respective institute needs to hold an institutional license. The documentation of the COSMO model is maintained by the COSMO-Consortium at https://www.
• Some reviews examine data, methodologies, and findings, whereas others look at constructs, themes, and theories. • Some reviews provide summaries by holistically synthesizing the existing research on a topic, whereas others adopt an integrative approach by assessing related and interdisciplinary work. The number of review articles published as independent or standalone studies has been increasing over time. According to Scopus (i. e. , search database), reviews (i. e. , document type) were first published in journals (i. e. , source type) as independent studies in 1945, and they subsequently appeared in three digits yearly from the late 1980s to the late 1990s, four digits yearly from the early 2000s to the late 2010s, and five digits in the year 2021 (Fig. 1 ). This increase is indicative that reviewers and editors in business and management research alike see value and purpose in review articles to such a level that they are now commonly accepted as independent, standalone studies.
Other parts of these procedures or protocols could also be reported more parsimoniously, for example, by filtering out documents, given that scientific databases (such as Scopus or Web of Science) have since been upgraded to allow scholars to select and implement filtering criteria when conducting a search (i. e. , criterion-bycriterion filtering may no longer be necessary). More often than not, the procedures or protocols of review studies can be signposted (e. g. , bracket labeling) and disclosed in a sharp and succinct manner while maintaining transparency and replicability. Other guides have been written to introduce review nomenclatures (i. e. , names/ naming) and their equivalent philosophical underpinnings. Palmatier et al. (2018)  introduced three clearly but broadly defined nomenclatures of literature reviews as independent studies: domain-based reviews, theory-based reviews, and methodbased reviews.
Based on their commonalities they can be summarized as follows: • Search database (e. g. , "Scopus" and/or "Web of Science") can be defined based on justified evidence (e. g. , by the two being the largest scientific databases of scholarly articles that can provide on-demand bibliographic data or records; Pranckutė 2021). To avoid biased outcomes due to the scope covered by the selected database, researchers could utilize two or more different databases  (Dabić et al. 2021 ). 2 Scoping reviews, structured reviews, integrative reviews, and interpretive/narrative reviews are commonly found in review nomenclature. However, the philosophy of these review nomenclatures essentially reflects what constitutes a good SLR. That is to say, a good SLR should be well scoped, structured, integrated, and interpreted/narrated.
This observation reaffirms our position and the value of moving away from review nomenclatures to gain a foundational understanding of literature reviews as independent studies. 3 Given that many of these considerations can be implemented simultaneously in contemporary versions of scientific databases, scholars may choose to consolidate them into a single (or a few) step(s), where appropriate, so that they can be reported more parsimoniously. For a parsimonious but transparent and replicable exemplar, see  Lim (2022). • Search keywords may be developed by reading scholarly documents and subsequently brainstorming with experts. The expanding number of databases, journals, periodicals, automated approaches, and semi-automated procedures that use text mining and machine learning can offer researchers the ability to source new, relevant research and forecast the citations of influential studies.
• Source type (e. g. , "journal") can be defined based on justified reasons (e. g. , journals selected because they publish finalized work; conference proceedings not selected because they are work in progress, and in business/management, they are usually not being considered as full-fledged "publications"). • Language (e. g. , "English") can be determined based on justified limitations (e. g. , nowadays, there are not many reasons to use another language besides the academic lingua franca English). Different spellings should also be considered, as the literature may contain both American and British spelling variants (e. g. , organization and organisation). Truncation and wildcards in searches are recommended to capture both sets of spellings. It is important to note that each database varies in its symbology.
• Quality filtering (e. g. , "A*" and "A" or "4*", "4", and "3") can be defined based on justified motivations (e. g. , the goal is to unpack the most originally and rigorously produced knowledge, which is the hallmark of premier journals, such as those ranked "A*" and "A" by the Australian Business Deans Council [ABDC] Journal Quality List [JQL] and rated "4*", "4", and "3" by the Chartered Association of Business Schools [CABS] Academic Journal Guide [AJG]). • Document relevance (i. e. , within the focus of the review) can be defined based on justified judgement (e. g. , for a review focusing on customer engagement, articles that mention customer engagement as a passing remark without actually investigating it would be excluded). • Others: Screening process should be accomplished by beginning with the deduction of duplicate results from other databases, tracked using abstract screening to exclude unfitting studies, and ending with the full-text screening of the remaining documents.
g. , keywords] that commonly appear together; PageRank analysis generates thematic clusters based on articles that are commonly cited in highly cited articles; and topic modeling generates thematic clusters based on the natural language processing of bibliographic data [e. g. , article title, abstract, and keywords]). 4 Given the advancement in algorithms and technology, reviews using bibliometric analysis are considered to be smart  (Kraus et al. 2021 ) and technologically-empowered  (Kumar et al. 2022b ) SLRs, in which a review has harnessed the benefits of (i) the machine learning of the bibliographic data of scholarly research from technologically-empowered scientific databases, and (ii) big data analytics involving various science mapping techniques  (Kumar et al. 2022c ). • Content analysis allows scholars to analyze a small to medium corpus of articles (i.
(2012) ,  Korherr and Kanbach (2021)  develop a taxonomy of humanrelated capabilities in big data analytics. Building on a sample of 75 studies for the literature review, 33 first-order concepts are identified. These are categorized into 15 second-order themes and are finally merged into five aggregate dimensions. Using the same procedure,  Leemann and Kanbach (2022)  identify idiosyncratic dynamic capabilities in a sample of 34 studies for their literature review. They then categorize these into 19 dynamic sub-capabilities. The advancement of technology also makes it possible to conduct content analysis using computer assisted qualitative data analysis (CAQDA) software (e. g. , ATLAS. ti, Nvivo, Quirkos)  (Lim et al. 2022a ). • Critical analysis allows scholars to subjectively use their 3Es (i.
2022a). The relationships established are often made up of conflicting evidence (e. g. , a positive or significant effect in one study, but a negative or nonsignificant effect in another study). However, through meta-analysis, scholars are able to identify potential factors (e. g. , contexts or sociodemographic information) that may have led to the conflict. • Others: Multiple correspondence analysis helps to map the field, assessing the associations between qualitative content within a matrix of variables and cases. Homogeneity Analysis by Means of Alternating Least Squares (HOMALS) is also considered useful in allowing researchers to map out the intellectual structure of a variety of research fields  (Gonzalez-Loureiro et al. 2015; Gonzalez-Louriero 2021; Obradović et al. 2021). HOMALS can be performed in R or used along with a matrix through SPSS software.
In summary, the overall objective of this analysis is to discover a low dimensional representation of the original high dimensional space (i. e. , the matrix of descriptors and articles). To measure the goodness of fit, a loss function is used. This function is used minimally, and the HOMALS algorithm is applied to the least squares loss functions in SPSS. This analysis provides a proximity map, in which articles and descriptors are shown in low-dimensional spaces (typically on two axes). Keywords are paired and each couple that appears together in a large number of articles is shown to be closer on the map and vice-versa. When conducting a literature review, software solutions allow researchers to cover a broad range of variables, from built-in functions of statistical software packages to software orientated towards meta-analyses, and from commercial to open-source solutions.
Personal preference plays a huge role, but the decision as to which software will be the most useful is entirely dependent on how complex the methods and the dataset are. Of all the commercial software providers, we have found the built-in functions of (i) R and VOSviewer most useful in performing bibliometric analysis (Aria and Cuccurullo 2017; R Core Team 2021; Van Eck and Waltman 2014) and (ii) Stata most useful in performing meta-analytical tasks. Many different analytical tools have been used. These include simple document counting, citation analysis, word frequency analysis, cluster analysis, co-word analysis, and cooperation analysis  (Daim et al. 2006 ). Software has also been produced for bibliometric analysis, such as the Thomson Data Analyzer (TDA), which Thomson Reuters created, and CiteSpace developed by  Chen (2013).
This study presents a Digital Terrain Model extraction method called Bulldozer. The only required input of Bulldozer is a Digital Surface Model generated from any sensors (usually optical or LIDAR) with any kind of software. After reviewing both the initial DrapCloth algorithm  (Zhang et al. , 2016)  and its multi scale implementation  (Leotta et al. , 2019) , some issues have been highlighted when extracting DTM from stereo satellite images such as the lost of ground adhesion under rising terrain areas, the appearance of sinks due to correlation issues when computing the DSM and finally the lack of scalability when processing large input data. Bulldozer has been developed to tackle all these issues and proposes a full automatic scalable pipeline composed of a pre-processing step to clean noisy DSMs by detecting and smoothing disturbed areas, a DTM extraction step based on a modified DrapCloth algorithm to stick to the ground under rising terrain and a post-processing step to smooth sharp sinks.
Needs A massive influx of 3D data seems to be emerging with new Earth Observation spatial missions such as CO3D (production at large scale of very high resolution DSMs over emerged landscapes(L. Lebègue, 2020) ), but also with the increase of airborne LIDAR DSMs 1. This trend motivates the French spatial agency (CNES) to focus on the development of tools to process 3D data at large scale. In this context, a DTM extraction software called Bulldozer has been designed to process DSM rasters at large scale without any exogenous data while being robust to noisy and no-data values. Another motivation was to handle any kind of sensor and all kind of topography (coastal, mountain, etc. ).
Workflow overview of the proposed method Bulldozer is a software that has been built around a pipeline of interchangeable modules. Each module is composed of functions with a simple API and very few dependencies. This architecture allows to call these functions in standalone mode outside the DTM extraction process (denoising, etc. ) but also to replace them by other functions doing the same task but with a different strategy. As shown in Figure  4 , the pipeline consists of three main modules: DSM preprocess, DT M extraction and DT M postprocess. These modules are composed of subfunctions generating the intermediate products that are used to produce a DTM in the end.
3. 2. 1 No-data processing Since Bulldozer does not take exogenous data as input, no-data values must be classified into several categories: border-no-data, inner no-data (occlusions) and disturbed no-data. The compute nodata module is used to differentiate the no-data areas that correspond to the edge of the image (border nodata) from those that are the result of uncertainties in the correlator during the DSM computation (inner nodata). To detect the border nodata pixels, we cannot simply detect the no-data pixels that are connected to the edge of the image. Indeed, as illustrated in Figure  6  if we apply this assumption, then the river would be considered as border nodata while it's no-data pixels that come from correlation errors due to water. Therefore, the border nodata detection method will try to delimit the border between inner nodata and border nodata as shown in figure  6.
Computation aspect The whole Bulldozer pipeline was designed to be fully scalable. To do this, all the processing steps are done by tiles. This approach allows the software to be executed in a distributed and parallel environnement. Bulldozer relies on the use of multi-processing in Python and the implementation of core functions in Cython in order to minimize the runtime and memory usage. Table  1  illustrates that Bulldozer can handle DSMs of abitrary sizes. The memory usage is linear with respect to the tile size (O(n)) where n is the number of pixels in the DSM.