dataesr/scientific-paragraphs-categorization huggingface dataset:
9 and 2. 2 g/cc). Spherical nanoparticles were then cut using Schrodinger's Maestro building tools  (Matestro, 2018). The silica surface was then hydrogen-terminated to avoid any erroneous coordination of the surface oxygen atoms. Packmol  (Martínez and Martínez, 2003; Martínez et al. , 2009)  software was used to pack the polymeric chains around the silica particle (Fig. 2 ) at a constant volume fraction of 5. 21%. The model structures obtained were then formatted into a LAMMPS data file. The binary system was characterized using a DREIDING  (Mayo et al. , 1990)  force field under the LAMMPS (http://lammps. san; Plimpton, 1995)  molecular dynamics platform. The use of the DREIDING force field was motivated by two points: i) the availability of established parameters for modeling silica and PMMA polymers and ii) the computational efficiency one can find in fixed-charge simulations of molecular systems.
Acknowledgment The authors would like to acknowledge the funding support from  Région Hauts de France  through the  INTIM  and  SENAREO  project ( RDIPROJFT63 -INV 31 ) and the helpful support in the computing process from  Guy Leon Kaza  ( Roberval Laboratory   FRE-CNRS 2012 ) and  Malik Abbassi  ( SAS Neoteckno ). Caltech received support from  DOE  ( STTR  Award  DE-SC0017710 ).
Materials and methods Our study encompassed the Peten Plateau along with portions of the RHFZ and the Coastal Plain. Over the course of the project geologic interpretation was performed at multiple scales, dependent on data availability and the scale of features being interpreted. We interpreted geologic structures and karst landscapes over the full extent of the PP (Fig  1 ). We also interpreted large-scale karst landforms over the study area using satellite data to enable characterization of the Mirador-Calakmul area (Fig 4A  and 4B ). In addition, the project identified small-scale karst features in the Mirador region throughout an area covered by Light Detection and Ranging (LiDAR) data (Fig  4A ). Helicopters were used for aerial survey of selected features and to traverse the heavily forested region.
Field methods During two field seasons in 2018 and 2019, field teams surveyed the karst landscape in the regions surrounding the archaeological sites of El Mirador, Tintal, Nakbe and the intervening areas. Prior to 2019, interpretation of LiDAR data served to identify karst landforms including collapse dolines, subsidence dolines, stream channels, swallow holes, faults, solution corridors, and other large-scale solution features. We up-loaded the GPS coordinates for these landforms to a Garmin inReach Explorer+ to assist in locating them on the ground in the jungle covered environment, and LiDAR images for key areas to a Garmin Montana to allow terrain visualization in dense vegetation while on site. In all, field teams visited over 250 sites during 2018-2019. Some 133 geologic samples were collected from sites that included limestones, dolostones, and gypsum as well as samples from specific features such as cements, brecciated zones, karst karren, and flowstone.
Of these, 83 samples were high graded for further analyses. Lastly, all remaining samples are archived in the sample warehouse at El Mirador. [77] , the upper Candelaria River Watershed referred to as the Calakmul Basin  [21] , and the Mirador-Calakmul Basin  [85]. These compare closely to the boundary of the MCKB defined below. Also noted is a natural buffer zone, the polygonal karst landscape, a region which Gunn and others defined as a military buffer between the Tikal and Calakmul regions during the Classic period  [21]. AW3D30 elevation data have been provided by JAXA (https://www. eorc. jaxa. jp/ALOS/en/aw3d30/) and printed under a CC BY 4. 0 license. https://doi. org/10. 1371/journal. pone. 0255496. g003.
Remote sensing data Regional visualization and geologic interpretation used elevation data recorded by the Japan Aerospace Exploration Agency (JAXA)  [87]. Takaku and others previously reviewed acquisition from the Advanced Land Observing Satellite (ALOS) and processing into a digital elevation model (DEM)  [88, 89]. Although the DEM was recorded at a horizontal resolution of 5 m, the publicly available data used in the present study, the ALOS World 3D-30m (AW3D30) was resampled to a 30 m resolution. To better understand the vertical accuracy of the data we validated the data using 60 Ground Control Points recorded by the Instituto Nacional de Estadı ´stica y Geografı ´a  [90, 91]  and 35 control points for bare earth well pad locations provided by the Guatemalan Ministerio de Energı ´a y Minas.
Although the overall Root Mean Squared Error was 5. 3 m, close to the nominal 5 m for AW3D30, when classified according to ground cover (bare, low trees or jungle) the error was 3. 2 m for bare earth, 5. 9 m for low trees, and 9. 0 m for jungle cover. This validation indicates that the AW3D30 is not sensing the ground level nor the top canopy in jungle covered areas but a level that is in the upper part of the canopy. Large-scale karst landforms and hydrologic features were imaged using multispectral data recorded by the European Space Agency (ESA) that allowed us to highlight vegetation changes that emphasized karst valleys and bajos. Drusch and others previously discussed Sentinel-2 data and reviewed the acquisition and processing of ESA data in detail  [92]. Sentinel-2, recorded in 13 bands with its MultiSpectral Instrument, including near-infrared with varying spatial resolution of 10 m, 20 m, and 60 m.
We utilized ESA's True Color Image (TCI) and our own Near-Infrared (NIR) data processed with QGIS software (Fig 5 and S1 Map), both with a 10 m resolution. Downloaded datasets included multiple years during dry periods which maximized vegetation differences  [93] [94] [95] [96]. The bulk of our analysis utilized data recorded in April 2019, near the end of the dry season, a time with low cloud cover. Data recorded during June 2020, after a heavy rain, served to illustrate stream overflow routes and ponding of water at springs  [97]. Visualization and interpretation of small-scale karst features utilized airborne LiDAR data collected by Eagle Mapping starting in 2015 using a Riegl LMS-Q1560 dual-channel system. The Phase I survey covered 645. 01 km 2 centered on the core sites of El Mirador, Nakbe, and Tintal located in the southern MCKB (Fig  4 ).
The survey design included a swath width of 641 m with an overlap of 55%, providing a nominal pulse density of 20 pulses/m 2. Total pulses were 16. 46 billion and total returns were 31. 21 billion with a return/pulse ratio of 1. 90. The average total return density after processing is 48. 39 points/m 2 and the average ground return density for the survey, as determined after classification with Terrasolid's TerraScan software, is 2. 18 points/m 2. Internal post-processing with Rapidlasso's LASTools software included further noise reduction, re-classification, and removal of zero-return areas resulting in an uplift to 2,875,496,652 ground points and a ground density of 5. 30 points/m 2. We created a final DEM using QuickTerrain Modeler, followed by Esri's ArcGIS software to generate a GeoTIFF raster with a horizontal cell size of 0.
5 x 0. 5 m. Accuracy of the final dataset is +/-15 cm horizontally and +/-30 cm vertically. A Phase II LiDAR survey recorded in 2018 has an average ground return density of 2. 95 and increased the total LiDAR coverage to 1703. 17 km 2 , covering 87. 2% of the Guatemalan portion of the MCKB. We utilized a variety of other cartographic and geospatial data over the course of the project. Published geologic maps guided our evaluation including the geologic map of Guatemala  [98]  and multiple individual map sheets by the Servicio Geologico Mexicano  [99] [100] [101] [102] [103]. We obtained hydrographical data for Campeche modeled by the Instituto Nacional de Estadı ´stica y Geografı ´a  [104] [105] [106] [107] [108] [109] [110] , for Peten modeled by the Ministerio de Agricultura, Granaderı ´a y Alimentacion  [111] , and a dataset covering the entire YP modeled by the USGS  [112, 113].
We also acquired a set of 61 orthophotographs covering the Calakmul region  [114]. These datasets provided insight into mapping watersheds, overflow streams, swallow holes, civales, and artificial reservoirs. Land clearing has obscured the region south and west of the PP in Guatemala making it difficult to identify original extent of natural wetlands. To overcome this obstacle, we accessed a map made by the US Defense Mapping Agency before whole scale deforestation  [115]. Esri ArcMap and Open Source QGIS were utilized for visualization, analysis, and interpretation of geospatial data. These included remote sensing data (AW3D30 DEM, Sentinel-2 multispectral, LiDAR), orthophotographs, geological data, hydrological data, and cultural information obtained from the Instituto Geografico Nacional of Guatemala and the Instituto.
Karst interpretation We interpreted large-scale karst landforms and hydrologic features, present throughout the MCKB and the surrounding areas, using an AW3D30 DEM  [87] , Sentinel-2 multispectral data  [94, 95] , and ESRI Online World Imagery (Table  1 ). On the TCI data the low lying bajos are dull brown areas juxtaposed against the brighter greener areas representing the upland forests. Dry season datasets highlight this distinction. The mapped bajos occur primarily in depressions when compared with the DEM data. The areas digitized show a system of both individual and coalesced bajos  [118]  which follow the KVs. Poljes, closed karst depressions with flat floors and a width greater than 1 km  [119] , are present sporadically across the MCKB as shown by the DEM data. These depressions typically host a bajo vegetation.
We documented their maximum closure with a polygonal outline. Numerous cockpits are present in the south and southeast margin of the PP. They show as sharp depressions on DEM data surrounded by rounded to conical hills. We recorded the cockpits as a point file and the crests of the surrounding hills as a polygonal network that covers much of the region. Residual hills are present along the southwestern margin of the PP and stand out against the level plain on DEM data. the hills present a green upland coloring on the TCI data while the intervening low areas present a mottled brown coloring like that of the bajos. We outlined the residual hills to indicate their coverage relative to the flat lying plain. Small lakes, or marshy civales common in the upland area along the MA, typically lie in closed depressions observed on DEM data nest within small to intermediate-sized bajos.
In most cases, open grasslands surround the lakes, which are apparent on the TCI data and Esri Online World Imagery. We delineated these lakes and their surrounding grassland areas as polygons to indicate their relationship to bajos on large-scale maps and converted this to a point file to show their distribution for small-scale maps. In many cases, the multispectral data over the bajos exhibit vegetation changes which illustrate the patterns of stream channels. We construed these patterns to show the overall drainage pattern. Some of the channels are throughgoing while many, however, appear to flow into closed depressions or dips in elevation. We infer these to represent intermittent streams and swallow holes and captured them accordingly. We identified abundant small-scale karst hydrologic features on LiDAR data recorded in the southern MCKB (Table  2 ).
Hydrogeologic modeling We compiled and classified hydrologic features, used to model inferring subsurface flow paths, using ESRI Online World Imagery, Sentinel-2 multispectral data, and orthophotographs (Table  3 ). Google Earth historical images allowed us to understand the presence of water features over time. For this aspect of our hydrogeologic research we worked over a broader area to show the regional nature of groundwater flow patterns. During the project, team members mapped and classified over 4000 individual features in ArcMap. These included wetlands, springs, residual pools, sinkhole ponds, lagoons, intermittent lakes, rivers, lakes, and ponds in half-blind valleys. An unclassified category included all water features not readily categorized. We extracted elevation from the AW3D30 DEM for all features and loaded the resulting dataset into the © Kingdom Software for gridding.
These studies utilized data from wells, wetlands, lakes, lagoons, civales, and sinkhole ponds all of which they considered to represent the regional water table. Our regional map showing the elevation of the regional water table over the southern PP and surrounding areas provides a more detailed view. As shown by the previous models, the water table slopes east and west from a high under the central plateau anticlines. We recognize that the level of the water table fluctuates from wet to dry seasons. Gondwe and others showed that this fluctuation was as high as 10 m in some areas of Quintana Roo and eastern Campeche  [39].
(  9 ) Basin boundaries are illustrated with a dashed line implying that the boundary is not exact, and sub-basin boundaries are shown with a dotted line indicating the boundary of a smaller unit. (10)  We identified residual pools in four locations along wet season, water fills the closed depressions, overwhelms the drainage conduits, and stagnates to form the seasonal swamps where it either evaporates or drains slowly into the subsurface. We evaluated the extent and distribution of the bajo system of the Mirador-Calakmul region showing a fluviokarst drainage pattern of KVs along with more isolated bajos in solution dolines and poljes. Prior authors have mapped the bajo system using aerial photos, topographic data, and remote sensing data  [32] [33] [34] 36, 154] , but have not covered the full extent of the basin.
Given the absence of dye tracer data we modeled the regional groundwater for the southern YP integrating over 4000 natural water points as outlined above. One steephead, El Chumpich Lagoon, is present in the interior of the basin. In the southern part of this region a residual pool is located where a karst valley, and its intermittent stream, exits the basin. Drainage along this overflow channel flows towards Arroyo Cuba to the west. A residual hill in the karst margin plain is present in the southwest. It is this region between the wetlands to the west and the seasonally dry uplands to the east that Scholes and Roys  [205]  referred to as the lacustrine belt. (B) Sentinel-2 TCI data from mid-June 2020 showing the La Amapola Lagoon and other springs filled with water following a storm event.
Karst valleys occupied by seasonal wetlands are the dominant landform. 2. Polygonal karst, a rugged cockpit and cone terrain dissected by dry valleys, is present along the southeastern margin of the plateau. The cockpits have large numbers of caves, collapse structures, and swallow holes that drain water quickly into the subsurface. 4. Upland karst, a landscape with numerous intermittent lakes and grassy wetlands, is present along the crest of the Mirador Anticline. 5. Polje karst at the northern end of the plunging Calakmul Anticline has scattered baselevel poljes, many with small wetlands, indicating control of the regional water table. We map key karst hydrological features that define the MCKB through integration of multiple forms of remote sensing data including orthophotographs, a satellite DEM, satellite TCI and NIR images, and a LiDAR survey as follows: 1.
In the southern part of this region a residual pool is located where a karst valley, and its intermittent stream, exits the basin. Drainage along this overflow channel flows towards Arroyo Cuba to the west. A residual hill in the karst margin plain is present in the southwest. It is this region between the wetlands to the west and the seasonally dry uplands to the east that Scholes and Roys [205]  referred to as the lacustrine belt. (B) Sentinel-2 TCI data from mid-June 2020 showing the La Amapola Lagoon and other springs filled with water following a storm event. Given that there is little surface catchment area associated with the lagoon we assume the springs sourced the water. AW3D30 elevation data have been provided by JAXA (https://www. eorc. jaxa. jp/ALOS/en/aw3d30/) and printed under a CC BY 4.
Acknowledgments Appreciation is extended to the  Ministerio de Cultura y Deportes of Guatemala  (MICUDE, Contract  DAJ 270-2018 ), the  Direccion General del Patrimonio Cultural y Natural , and the  Instituto de Antropologı ´a e Historia de Guatemala (IDAEH)  for the authorizations and permits to carry out this research. Field work in the southern Mirador-Calakmul Karst Basin is part of the ongoing Mirador Basin Project directed by  Richard D. Hansen  under the auspices of the  Department of Anthropology of the University of Utah  and the  Foundation for Anthropological Research and Environmental Studies (FARES). We are appreciative to all the individuals, foundations and institutions that have provided funding to FARES for this research. We are thankful to the  Direccion General de Aeronautica Civil of Guatemala  ( Contracts DGAC/ FA -1-2009 ,  016-2014 ) for the necessary flight permits and authorizations.
Unlike existing topic modeling algorithms wherein the inferred topics are not identifiable, MixEHR-G uses prior information from informative surrogate features to align topics with known phenotypes. We applied MixEHR-G to an openly-available EHR dataset of 38,597 intensive care patients (MIMIC-III) in Boston, USA and to administrative claims data for a population-based cohort (PopHR) of 1. 3 million people in Quebec, Canada. Qualitatively, we demonstrate that MixEHR-G learns interpretable phenotypes and yields meaningful insights about phenotype similarities, comorbidities, and epidemiological associations. Quantitatively, MixEHR-G outperforms existing unsupervised phenotyping methods on a.
Several methods employ regression with regularization or dropout to de-noise silver-standard labels using other features  [20, 21] , but this strategy tends to be sensitive to high feature dimensions and sparsity typical for EHR data. Topic modeling algorithms are well-conditioned to this setting. Designed to model highlysparse text data with vocabularies often consisting of tens of thousands of words, topic models have shown considerable aptitude at discovering latent structure (i. e. topics) in such datasets  [26] [27] [28] [29]. Indeed, several studies have demonstrated success applying derivatives of the widely used topic model Latent Dirichlet Allocation (LDA) to EHR data, considering patients as documents, disease phenotypes as topics, and EHR features as words  [26, 27]. One notable model, MixEHR, builds upon LDA to 1) simultaneously model an arbitrary number of data modalities (e.
g. , ICD codes, lab results) with modality-specific distributions, and 2) treat observations as not missing at random (NMAR), reflecting the fact that clinicians typically collect data such as lab tests with a diagnosis in mind  [27]. However, MixEHR is fully unsupervised and thus prone to learning topics that lack clinical meaning. On the other hand, the recent sureLDA method constrains topics to align with well-defined phenotypes using a weakly supervised surrogatebased approach, thereby learning meaningful, interpretable topics  [26]. However, sureLDA treats all features as draws from topic-specific multinomial distributions, rendering it unable to properly model other data modalities such as lab tests and vital signs. Moreover, its Gibbs Sampling inference procedure does not scale well to datasets with more than thousands of features or a few hundred thousand patients.
In this study we propose MixEHR-Guided (MixEHR-G), an adaptation of MixEHR that employs the guided topic modeling approach of sureLDA to align inferred phenotype topics with well-defined disease phenotypes. We apply MixEHR-G to the PopHR dataset  [30] , which contains longitudinal administrative claims data for a random sample of 1. 3 million residents of Quebec, Canada (Methods), and to the MIMIC-III dataset  [31] , a publicly available dataset of Intensive Care Unit (ICU) observations at the Beth Israel Deaconess Medical Center in Boston, Massachusetts. In both applications, MixEHR-G is able to effectively infer meaningful phenotype distributions over high-dimensional, multimodal EHR data including ICD codes and prescriptions. We use MixEHR-G's patient-topic mixture distributions to predict patient phenotypes and to analyze disease prevalence as a function of age and sex.
MixEHR-G and EHR data overview Building upon MixEHR, MixEHR-G uses a probabilistic joint topic modeling approach that projects a patient's high-dimensional and heterogeneous clinical record onto a low-dimensional latent topic mixture membership over disease phenotypes. In contrast to MixEHR, MixEHR-G incorporates Phenotype Codes (PheCodes), expert-defined groupings of ICD-9 codes that have been shown to more closely align with disease phenotypes described in clinical practice than individual ICD-9 codes  [32] , as topic priors for each phenotype to guide posterior topic inference. In this way, MixEHR-G harnesses the unsupervised topic modeling prowess of Mix-EHR to learn more interpretable and well-defined phenotypes. We applied MixEHR-G with phenotype topics defined by the 1515 unique integer and 1decimal PheCodes to the PopHR and MIMIC-III datasets described in the Introduction  [30, 31].
Besides the distinct population coverage of the two datasets, they also involve different feature modalities and levels of disease acuity (Methods). Briefly, PopHR contains both chronic and acute disease information from outpatient visits as recorded in ICD codes, administrative treatment (ACT) codes, and RxNorm medication codes. MIMIC-III contains highly acute disease information from ICU admissions, often with comorbid or causative chronic diseases (i. e. flash pulmonary edema secondary to heart failure in a patient with chronic hypertension), as recorded in clinical notes, ICD-9 billing codes, prescriptions, DRG billing codes, CPT procedural codes, lab tests, and lab results. We used these two datasets to demonstrate the generalizability of our MixEHR-G and gain contrasting insights between the datasets regarding the characterization of a common set of diseases.
MixEHR-G's Phenotype topics are clinically meaningful We qualitatively explored the inferred phenotype topics from both the PopHR and MIMIC-III datasets by examining the EHR features with the highest topic distribution probabilities (φ k 's) for 9 diverse chronic diseases covering a variety of medical specialities (Fig. 2 ; Supplementary Fig. S2 ). We observe that the phenotype topics appear to be predominantly defined by prescription (Rx) codes in the PopHR dataset (Fig. 2a ), whereas in the MIMIC-III dataset they are dominated by ICD-9 codes (Fig. 2b ). This highlights the ability of MixEHR-G to leverage diverse EHR modalities to infer phenotype topics in a data-driven manner as opposed to relying solely on the ICD-9 signatures for each PheCode. In contrast, the top features identified by sureLDA are mostly ICD-9 codes for PopHR and words from clinical notes for MIMIC-III, for which words account for 33,388 of 53,432 total features (Supplementary Fig.
The only phenotype for which sureLDA outperformed MixEHR-G was epilepsy, for which no phenotyping algorithm aligned particularly well with the rule-based labels (i. e. all AUPRCs were below 0. 25). Nonetheless, MixEHR-G consistently outperformed the baseline methods including the raw PheCode counts and MAP. Since both baselines only draw upon a handful of ICD-9 codes per phenotype, the superior performance of MixEHR-G further corroborates the benefit of leveraging the vast amount of additional information in patients' EHRs. To investigate the concordance and discrepancy between MixEHR-G's inferred topics and the existing phenotyping rules, we focused within the PopHR dataset on chronic obstructive pulmonary disease (COPD), a notoriously difficult phenotype to define with rules (Fig. 5 ; Supplementary Fig.