dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, sites on the World Heritage Tentative List are not provided with maps or coordinates. The World Database on Protected Areas  53  was loaded into a geographic information system to identify missing polygons for Ramsar and Natural World Heritage Sites. Maps of sites were overlaid onto Google Earth using the image overlay function  [54] [55] [56]  , allowing delineation of each site's boundaries. When sites were not identifiable, due to either land cover or missing/ inaccurate information, we used historical imagery and/or published literature to delineate the site accurately  57, 58. This resulted in a polygon shapefile containing the master table of all sites (see Extended Data Fig. 1  and the publicly available dataset in the Data availability section). The database includes the names and locations of the sites, their officially designated heritage site number and their geospatial outline, as well as the site area and the mean, minimum and maximum elevation of each site.
The total area of all the sites of interest is 512,757 km 2. In the present analysis of coastal exposure, sites found at elevations below 50 m are included, this is a conservative inclusion criteria to ensure all sites potentially exposed to rising seas are included. The filtering was done using elevation data from the 3 arcsec GLO-90 digital elevation model (DEM) available from the Copernicus services  59  , and the threshold elevation is sufficiently high to ensure that no site is erroneously excluded due to vertical bias of the DEM (in the worst case, a few metres). Heritage site delineations used here are constrained to the physical characteristics of the site that were remotely sensed for the purpose of coastal flood analysis. However, heritage can be framed in multiple ways and is defined differently across disciplines.
Coastal erosion. Projections of shoreline change driven from ambient factors, relative sea-level rise and episodic erosion during extreme storms are available from ref. 14. The projections are probabilistic, providing full probability density functions every 10 years until 2100. Shoreline change is the combined result of three components: (1) ambient shoreline dynamics driven by long-term hydrodynamic, geological and anthropic factors  75, 76  ; (2) shoreline retreat due to relative sea-level rise, estimated using a modified version of the Bruun rule  14  ; and (3) episodic erosion during extreme storms (as with the floods, we focus on the 100-year event), estimated after detecting extreme events from global wave projections datasets and simulating beach profile response at each global location (the analysis includes millions of simulations and is described in detail in ref.
14. The existing projections of ref. 14  express potential shoreline change, assuming infinite amount of sediment supply and accommodating space for coastal retreat at the backshore. As a result, additional effort was used to identify where and to what extent shoreline retreat in the vicinity of heritage sites would be interrupted by the presence of non-erodible features (for example, seawalls, revetments) and other specific geological conditions. Starting from the dataset on the spatial distribution of sandy beaches along the African coastline from ref. 76  , we consider additional information to identify which sites are actually exposed to coastal erosion. The Global Lithological Map  77  is the most accurate dataset describing the properties of surface rocks worldwide and is used to identify rocky coastlines, while additional natural and man-made obstacles to shoreline retreat were identified through carefully inspecting the time history of satellite images from Google Earth at all sites.
Existing studies have shown that heritage can be exposed to several natural hazards such as fires, river floods and earthquakes, among others  5, 78. Also, there are some previous efforts to quantify the exposure through indicators (for example, https://cvi-heritage. org). In this continental scale assessment, we focus on only the coastal hazard exposure of AHS, trying to provide the most accurate, quantitative information as possible given the available datasets. We use the exposed area as indicator, since the available datasets do not allow quantifying other proxies, such as economic losses. Important to heritage valuation, there is a lack of data on the intangible qualities of AHS, such as Indigenous knowledge, making assessment of their exposure and potential loss or damage from climate change not possible at this scale of research.
In addition, we lack the information on how components are distributed inside each site, and therefore we cannot quantify to what extent the impact of exposure among different areas of the same site varies. The above would be very interesting directions for future research  79  , but there are no available data allowing quantification of the economic value of AHS. As do most global assessments, our analysis contains inevitable limitations in terms of methodological abstraction and data accuracy/resolution. DEMs are known to be a dominant factor of uncertainty  80, 81  and there are several publicly available global datasets that aim to improve that aspect  [82] [83] [84]. In this analysis we use GLO-90, which has been recently published by the European Union's Earth observation programme. GLO-90 combines several existing DEMs (https:// spacedata.
copernicus. eu/web/cscda/dataset-details?articleId=394198), among which are elevation data provided from X-band radar, proven to be capable of achieving better accuracy, compared with Shuttle Radar Topography Mission (SRTM) variants. Therefore, we are confident that GLO-90 is a reliable choice for the DEM. Another assumption is that we consider coastal flooding independently from river flooding or precipitation, despite the fact that these factors, when acting together with non-climatic threats  85  , can have a larger compound effect  11. However, despite recent advances  86  , incorporating all the above components in a pan-African assessment is still challenging. For that reason, we focus only on marine flooding, omitting terrestrial and groundwater flows, as it is also unknown to what extent the latter would affect inundation along AHS.
Birgitt Ouweneel 8 , Salma Sabour 9  , Carley E. Iles 10, 11  , Christopher H. Trisos 8, 12  , Luc Feyen 13  , Lorenzo Mentaschi 14  and Nicholas P. Simpson 8 ✉ The African coast contains heritage sites of 'Outstanding Universal Value' that face increasing risk from anthropogenic climate change. Here, we generated a database of 213 natural and 71 cultural African heritage sites to assess exposure to coastal flooding and erosion under moderate (RCP 4. 5) and high (RCP 8. 5) greenhouse gas emission scenarios. Currently, 56 sites (20%) are at risk from a 1-in-100-year coastal extreme event, including the iconic ruins of Tipasa (Algeria) and the North Sinai Archaeological Sites Zone (Egypt). By 2050, the number of exposed sites is projected to more than triple, reaching almost 200 sites under high emissions.
Data availability The models and datasets presented are part of the integrated risk assessment tool LISCoAsT (Large Scale Integrated Sea-level and Coastal Assessment Tool) developed by the Joint Research Centre of the European Commission. The African heritage sites list, as well as the coastal hazard assessment data, are available through the LISCoAsT repository of the Joint Research Centre data collection (http://data. jrc. ec. europa. eu/collection/LISCOAST) from https://data. jrc. ec. europa. eu/dataset/31e2737e-2059-4f47-b088-16db7a09a555. Heritage sites can be identified by their designated name and number in the data, and the centroid of the site is provided. We do not provide the polygons showing the extent of each site due to data sensitivities, but these can be provided by the corresponding authors on reasonable request for non-commercial use.
5 , 5 Birgitt Ouweneel 8 , Salma Sabour 9  , Carley E. Iles 10, 11  , Christopher H. Trisos 8, 12  , Luc Feyen 13  , Lorenzo Mentaschi 14  and Nicholas P. Simpson 8 ✉ The African coast contains heritage sites of 'Outstanding Universal Value' that face increasing risk from anthropogenic climate change. Here, we generated a database of 213 natural and 71 cultural African heritage sites to assess exposure to coastal flooding and erosion under moderate (RCP 4. 5) and high (RCP 8. 5) greenhouse gas emission scenarios. Currently, 56 sites (20%) are at risk from a 1-in-100-year coastal extreme event, including the iconic ruins of Tipasa (Algeria) and the North Sinai Archaeological Sites Zone (Egypt). By 2050, the number of exposed sites is projected to more than triple, reaching almost 200 sites under high emissions.
Results The dimensions and formation of the crater are similar to previous work  (Collins et al. , 2008; Morgan et al. , 2016). The results of our "fiducial" hydrocode impact simulation, with an assumed seafloor depth of 1 km and a run time of 10 min, are shown in Figure  1. About 2. 5 min after contact of the projectile, a curtain of ejecta pushing water outward from the impact produced a 4. 5-km-high wave (Figure  1a ). After 5 min, falling ejecta continued to impart momentum to the ocean (Figure  1b ). At 10 min, after all the ejecta had fallen, a 1. 5-km-high wave, known as a rim wave, located 220 km from the point of impact was left propagating throughout the deep ocean (Figure  1c ). Note that the majority of the wave breaking in the iSALE simulation takes place before the 10 min endpoint.
The axisymmetric nature of our high-resolution hydrocode model requires an ocean layer with a constant water depth. The ocean at the point of impact is estimated to be 100-200 m deep  (Gulick et al. , 2008) , and it becomes deeper toward the northwest. Generation of the tsunami rim wave, however, is sensitive to the ocean depth at the crater rim, not at the point of impact. Paleobathymetry estimates indicate that water depth was ∼1 km where ejecta emplacement produces the initial rim-wave (50 km from basin center). At ∼150 km from the point of impact the ocean was ∼3 km deep (Figure  S1  in Supporting Information S1). To test for sensitivity of the rim wave and crater shape to pre-impact ocean depth we vary the thickness of the ocean layer from 1 to 3 km. The waveforms after the first 10 min of the fiducial simulation, and after the first 10 min of iSALE simulations with different water depths, are displayed in Figure  2.
While MOM6 is run for all of the cases shown in this paper, MOST is run only in the fiducial case described below. Both tsunami propagation models used the same global 1/10th degree bathymetric grid (Tables  S2  and S3  in Supporting Information S1). To accurately simulate tsunami propagation, a global Maastrichtian (66 Ma) paleobathymetry is combined with the initial condition from the hydrocode results. The sources for the paleobathymetry are  Müller et al. (2008)  and  Scotese (1997). More information about the bathymetries that we combined can be found in Text S1 of Supporting Information S1. To continue the simulation with the tsunami propagation codes we convert the axisymmetric, constant water depth hydrocode results (see Figure  S2  in Supporting Information S1) to more realistic, non-axisymmetric conditions with horizontally varying resting water depths.
Methods Identifying the K-Pg boundary in marine sections requires some form of stratigraphic information. This is usually provided by biostratigraphic or paleomagnetic investigations. Marine sections located above present-day sea level and exposed on land usually allow a broad view of boundaries in outcrop and extensive stratigraphic data can often be collected from the section. Based on these studies and the overall preservation and exposure of the interval, one section has been named the "type section. " The stratotype section for the K-Pg boundary is at El Kef, Tunisia  (XXVIIIth International Geological Congress, 1989). The boundary itself has been linked to the anomalous abundance of Iridium that was derived from the impacting body. Close to the impact site reworked sedimentary deposits are mixed with ejecta from the impact.
The method selects the most informative variables which are invariant to the differences in the instruments, physical or chemical state of the samples and the differences in the environmental factors around the experiment. The method is inspired by domain invariant partial least-square (di-PLS) and the covariate selection (CovSel). The potential of the method is demonstrated on four real cases related to the calibration of near-infrared (NIR) spectroscopy on agri-food materials. The results show that in all the cases, the domain invariant features selected by the di-CovSel have low prediction error compared to the standard variable selection with the CovSel approach when the models are tested on a new data domain. In summary, domain invariant features selected across domains support the development of calibration models with good generalization and supply a better understanding of the system by bypassing the external factors originating from differences in the instruments, physical or chemical states of the samples and the differences in the environmental factors around the experiment.
Thanks to this similarity, it could be possible to make it domain invariant based on the principles of di-PLS  [27] [28] [29]. Therefore, the aim of this study was to develop and test a domain invariant version of the CovSel variable selection method. The development of di-CovSel is inspired by the existing di-PLS and CovSel variable selection approaches. The main hypotheses of the study are as follow: Just like the standard PLS model that suffers from unseen variation present in the new domains, the models based on CovSel selected variables will not generalize well to the new domain. Adding domain invariance to CovSel will allow to select variables that provide models which generalize better to the new domain of the data. To test the above two hypotheses, CovSel and di-CovSel were compared on a wide range of NIR data sets.
Different physical forms of rice samples The rice data set was used to show the capability of the di-CovSel model to select the variables that allow a model to be used in a different physical form of samples. The source was the solid rice kernels, while the target was rice powder. This data set was first published in Ref. [36]  and later used in Ref. [22]  to show the application of di-PLS for domain adaption of the model made on rice kernels to use it on rice powder. The data set consists of NIR spectra measured on 200 individual rice kernels followed by spectral measurement on the ground rice kernels in powder form. The spectral measurements were performed with a FT-NIR spectrometer (MPA, Bruker, Germany). The reference property was the protein content which according to the primary study was measured using the Dumas combustion method  [36].
Wheat data set for model adaption from point to spectral camera for digital phenotyping The wheat data set was used to show the capability of di-CovSel to select variables that lead to generalized models between different modes of Vis-NIR spectroscopy. The source was the point spectrometer, while the target was the spectral camera. The data set is the same as published in Ref. [37]  and later used in Ref. [38]  to show the capability of calibration transfer to transfer models from point spectrometers to spectral cameras. It consists of spectral and reference nitrogen content measurements performed on 200 plants at 3 different time points. The spectral measurements were performed in the Plant Accelerator facility at the University of Adelaide, Australia. To obtain wide nitrogen variation in plants, the plants were treated with four treatments of 25, 50, 100 and 200 mg N/kg.
The point spectral measurements were performed with a combination of a diode array with a monochromator in the 400-2500 nm range (FieldSpec 3, Analytic Spectral Devices, Boulder, USA) with a leaf clip, and the spectral imaging was performed using a high-throughput spectral imaging setup (WIWAM, Ghent, Belgium) with Vis-NIR and SWIR spectral cameras from Specim, Finland. The data set contains 600 spectral measurements for each spectral sensor and 600 reference values for the N content (%). Based on the earlier study, 57 measurements were outliers and removed as suggested in Ref. [37]. Finally, the data set had 543 spectral and 543 reference nitrogen measurements.
Moisture prediction in pear fruit of different seasons The pear data set was used to show the capability of di-CovSel to adapt models based on different seasons for fruit moisture content (MC) prediction. Currently, the season variability is one of the main challenges in NIR spectroscopy of fruit which leads to model failure. The pear data set consists of pear 'Conference' fruit harvest of two seasons 2019 (season 1) and 2020 (season 2), measured with a portable fruit spectrometer (Felix F-750, Camas, WA, USA). This data set is the same as used in Ref. [39]. Season 1 data consists of spectra and reference MC measurements on 239 pear fruits, while the season 2 data set consists of 230 fruits. Spectral measurements were performed at the center belly part of the fruit. For reference MC measurements, a 1 cm thick slice was cut from the fruit equator and divided into four equal parts.
Model transfer between point spectrometers to predict moisture content in mango fruit The mango fruit data set was used to demonstrate di-CovSel for using free access spectral datasets for local use in new experiments. Basically, it is a case of standard free calibration transfer where the free access dataset measured with a similar instrument was generalized to a new local instrument. The free access data set used in this study is the mango data set  [40] [41] [42]  for which both spectra and reference moisture content values are available. Please note that the mango data set is based on mango samples measured in Australia. The local mango data set measured with a similar but different instrument was the same as used in Ref. [43]  and consisted of spectral and moisture measurements performed on 501 mango fruit of 'Keitt' and 'Kent' cultivar performed at Wageningen University & Research, The Netherlands.
The instrument model used for the acquisition of the open-access and local data set was a portable fruit spectrometer (Felix F-750, Camas, WA, USA). For MC measurements, the peel of the part was removed and later the fresh weight of the fruit's flesh was measured using an electronic balance and dried in a hot-air oven (FP 720, Binder GmbH, Tuttlingen, Germany) at 80 C for 96 h. After drying, the dried fruit weight was measured, and the MC was estimated and expressed in %. The spectral range used for modelling was the same (684-990 nm) as recommended in earlier publications  [40, 42, 43]  on the open-access mango data set. Please note that the original free access mango data set has more than 10k measurements. However, in this study we only used measurements from the year 2016. Since we used data from season 2016 and the new data set measured in a local setting based on fruit from season 2020, the mango case also forms a basis for model adaptation related to seasonal differences.
Data analysis The datasets of the source domain were randomly divided into calibration and test sets with a 70-30 rule. A detail on total samples and before and after the partition is provided in Table  1. The datasets from the target domain were randomly divided into target and test targets with a 30-70 rule. The minority was chosen for the sake of using as few samples from the target domain as possible during domain invariant variable selection. Calibration models were trained using ordinary least squares (OLS) with the selected variables based on the calibration data of the source domain and tested on the source and target domains for CovSel and di-CovSel. To account for a suitable bias term in the OLS models to predict the target domain, recentering of the source domain was performed. For this, 10 samples were selected from the adaptation target data with a Kennard Stone  [44]  algorithm.
Please note that in real-life situations there is usually very little information available about the difference between scenarios, for example, in the case of seasonal differences, the underlying cause is hardly known due to the high biological variability. Hence, a major focus of the scientific community working on fruit spectroscopy is to bypass the difference  [21] [22] [23] , which is also explored with the di-CovSel approach proposed in this study. The properties of the different data sets are summarized in Table  1. The reference property ranges for the target domain for all data sets were in the range of the target property ranges of the source samples. Also, note that the di-CovSel method was applied in this study using as low as 60 samples for the rice data set to up to a maximum of 150 samples for the mango data set.
Sample sizes of 60-150 for model adaptation without the need for new reference property analyses can be considered as a profitable situation in practice, as typically the reference analysis is the main limiting factor for model adaptation with traditional chemometric approaches  [25]. The optimizations of the CovSel and the di-CovSel for all four data sets are illustrated in Fig. 2. It should be noted that for di-CovSel there is an extra λ parameter which needs to be tuned and in Fig. 2  corresponds to the distinct color error curves. For the rice data set (Figs. 2A), 10 variables were selected for CovSel and di-CovSel based on the evolution of the cross-validation error as a function of the number of selected variables. The domain invariant regularization parameter was set to 10 6 as the RMSECV values started to increase for larger values.
2B ) suggested that the optimal number of variables by CovSel was 16, which was similar for values of λ < 5:10 6 in di-CovSel. For larger values of the domain invariant regularizer, the optimal number of variables became more clearly ~11. As the aim was to compare di-CovSel and CovSel for selecting variables that generalize well across different modalities of NIR spectroscopy i. e. , point spectroscopy and spectral imaging, the models were created with the same number of selected variables for CovSel and di-CovSel. The OLS model made with CovSel selected variables on point spectroscopy data (Fig. 3D ) performed poor when used on the spectral imaging data (Fig. 3E ). The prediction performance showed a large dispersion together with a clear problem of bias. However, with di-CovSel the RMSEP was decreased from 1.
29% to 0. 90%, showing the bias problem (Fig. 3E  and F ) was fully corrected and a lower dispersion in the predictions was obtained. This result suggests the ability of di-CovSel to select better generalizing variables when the calibration model is to perform across different modalities of NIR spectroscopy, especially for the correction of bias in the predictions. For the pear data set, the optimal number of variables appears to be 7 for CovSel and for di-CovSel with values of λ < 10 8. The effect of the λ value on the optimal number of variables can be noted as some smaller values of this parameter suggested to use 6 variables. The resulting performance of CovSel is illustrated in Fig. 3H  and in Fig. 3I  for di-CovSel with the same number of variables i. e. , 7 and setting λ ¼ 10 7. The OLS model made with equal number of variables showed that di-CovSel achieved a lower RMSEP of 0.