dataesr/scientific-paragraphs-categorization huggingface dataset:
Wetland extents Figure  11  shows that the global wetland fractions of the different CW maps are in range of those in  Fan et al. (2013)  and  Hu et al. (2017) , with twice the wetlands in GLWD-3, itself 60 % smaller than the RFW extent. Over France (Fig. 11b ), the wetland fraction of the CW maps is notably similar to that of MPHFM, which is a calibrated and validated wetland dataset that includes the GDWs. The regional uncertainty of CW maps is smaller over subtropical areas and higher over boreal and tropical zones. For instance, although the global wetland extents of CW-WTD and CW-TCI15 are nearly equal, the former contains 52 % more wetlands over the Hudson Bay Lowlands. However, in Southeast Asia, where RFWs have a rather large contribution to total wetlands, CW maps are in relative agreement on wetland extents, whereas the validation dataset appears to critically underestimate the wetland extents.
The underestimation of global validation datasets, especially GLWD-3, is quite clear in France, the Amazon and the Ob River basin. Nevertheless, regional differences in wetland fractions among CW maps reaching up to 25 % in the HBL and the Amazon basin (due to the effect of permafrost in northern latitudes and high effective precipitation over the tropics) make our estimates uncertain as well. Additionally, the uncertainty of the reference validation datasets is almost always higher than that of CW maps (global: CW 7 %, validation 17 %; Ob basin: CW 25 %, validation 32 %).
Uncertainties of the CW maps and underlying layers It must be stressed that the uncertainty of the proposed CW maps is high, owing to several factors impeding the accuracy of the RFW and GDW maps. The uncertainty of the RFW map comes from the three input layers (ESA land cover, GIEMS-D15 and JRC surface water) and the lack of accuracy of the remote sensing products they rely on (shown by their large range of global flooded extents, from 1. 5 % to 7. 7 % excluding lakes). Of particular relevance is the uncertainty of GIEMS-D15, which contributes a lot to the high fraction of RFWs, and exhibits a small overlap with the other two datasets (less than 10 % of inundated areas in GIEMS-D15 are confirmed by either ESA land cover or JRC surface water). Taking GLWD as a reference,  Adam et al. (2010)  concluded that inundation extents are overestimated in GIEMS (0.
S2, Figs. S3  and S4 ). We also considered several classic variants of the TI to conclude that the TCI (topography-climate wetness index), also favored by  Hu et al. (2017)  with a modified formula, offers the best correspondence with the validation datasets. The original TI did not capture the wetland density contrasts between arid and wet areas, and the inclusion of subsurface transmissivity in TCTrI induced overly sharp density contrasts that did not always match the recognized patterns of large wetlands. This does not question the role of transmissivity in forming wetlands, but calls for improved global transmissivity datasets or new methods to supply a more continuous description of transmissivity than those currently proposed based on discrete classes of lithology  (Hartmann and Moosdorf, 2012; Gleeson et al.
, 2014)  or soil texture  (Fan et al. , 2013). Particular attention also needs to be given to the effect of permafrost on wetland formation, but simple maps are probably not sufficient to describe the complexity of hydrology-permafrost feedbacks, especially under global warming  (Walvoord and Kurylyk, 2016). The resolution of the input datasets is also prone to errors if coarser than the target wetlands. It is the case for transmissivity, as discussed above, and for climate input, at the 0. 5 • resolution for both GDW-TCI and GDW-WTD, which may lead to anomalous discontinuities, although they are not discernible in Fig. 2a, c, f. More relevant is the resolution of topography, at 15 and 30 arcsec for the TI calculation  (Marthews et al. , 2015)  and WTD modeling  (Fan et al. , 2013) , respectively.
An important consequence is that the pixels of our 15 arcsec wetland maps are either fully wet or fully non-wet, which is obviously wrong in many places with patchy wetlands in small depressions or along headwater streams. A finer delineation can be expected from higher-resolution DEMs, such as HydroSHEDS or the MERIT (Multi-Error-Removed Improved-Terrain) DEM of  Yamazaki et al. (2017) , both offering a worldwide 3 arcsec resolution. Finally, it must be underlined that the RFW, GDW and CW maps largely overlook the loss of wetlands induced by anthropogenic pressures, estimated to affect 30 % to 50 % of undisturbed or potential wetlands  (Finlayson et al. , 1999; Sterling and Ducharne, 2008; Hu et al. , 2017) , mostly due to urbanization and agricultural drainage. This feature is especially true for GDWs because most human influences were neglected in the input datasets (climate, topography, transmissivity and sea level) for global WTD modeling.
In contrast, the RFW map was derived by overlapping satellite imagery for the contemporary period (past 5 to 34 years), thus showing most human-induced changes on the surface water, including artificial wetlands linked to flooded irrigation  (Adam et al. , 2010)  or the way in which damming shifts wetlands to lakes or drylands  (Pekel et al. , 2016). Nevertheless, the overlap of several inundation datasets with different historical depths was intended to minimize these disturbances, as justified by the higher spatial correlation between the inundation datasets and the CW maps than between themselves. Therefore, by construction, the proposed CW largely corresponds to potential wetland. Considering that the loss of natural wetlands exceeds the extent of artificial ones by far, they have a larger extent than actual wetlands, making validation all the more complicated.
Selection of two representative CW maps If none of the resulting CW maps systematically overperform the others, two of them usually display the best similarity scores, namely, CW-TCI15 and CW-WTD (Fig. 4 , Tables  5  and S1-S7 ). These two datasets (hereafter simply referred to as "CW maps") have many similarities, and by construction, they have almost the same wetland extent (ca. 21 %), and the combination with RFWs reduces the differences found between the corresponding GDWs in boreal and tropical areas (Fig. 3 ). Both CW maps are among the highest estimates of global wetland, considerably larger than GLWD-3 and close to  Hu et al. (2017). An interesting point is that the SPC between these two CW maps and the existing wetland datasets is higher than the SPC among these existing datasets (Table  5 ), which is rather low (e.
Zonal patterns Despite many similarities, the zonal distributions of the CW maps, RFWs and validation datasets are sometimes different. Generally, wetland datasets such as GLWD-3 and GDW-WTD appear to underestimate global wetland extents with respect to CW maps (Fig. 12  and the visuals for France and Southeast Asia, Figs. 5  and 7 ). The latitudinal patterns are also different among maps in Fig. 12 , particularly over the tropics and the boreal zones. Although the wetlands in all validation datasets and CW-WTD are densely concentrated between 50 and 60 • N, in the RFW map, the northern subtropical (25-35 • N) and boreal (60-70 • N) wetlands are of a similar extent (1. 9 and 2. 0 million km 2 ), and in CW-TCI (15 %), tropical wetlands (10 • N-10 • S) globally outweigh others (covering almost 9 million km 2 ).
Data availability and application The dataset consisting of the two selected composite wetlands maps (CW-WTD and CW-TCI (15 %)) is supplied in raster format at 15 arcsec resolution through PANGAEA https://doi. org/10. 1594/PANGAEA. 892657 (Tootchi et al. , 2018). Pixels located in oceans and glaciated lands of Greenland are assigned NoData, whereas the remainder of land is split into four classes with distinct codes for non-wetlands, the intersection of GDWs and RFWs and "pure" RFWs and GDWs. All of the datasets used as input to the generation of these dataset are available via open access for research and educational applications and can be accessed through the web links mentioned in their accompanying scientific papers. These classified maps are believed to be useful for hydrological or land surface modeling by assigning specific prop-erties or processes to the places identified as wetlands or floodplains.
Conclusions and perspectives In an effort to develop a comprehensive global wetland description, we merged regularly flooded wetlands (RFWs) and groundwater-driven wetlands (GDWs) to develop composite wetland (CW) maps, under the assumption that both RFWs and GDWs are relevant although not exhaustive. The corresponding maps were produced globally at high resolution and two CW maps were selected based on comparisons with global and regional evaluation datasets. Their validity is particularly supported by the good match with the MPHFM dataset developed by  Berthier et al. (2014)  over France because it was tailored to comprehensively include flooded and non-flooded wetlands with calibration against hydromorphic soils and validation against local surveys. With a total wetland fraction of around 21 % of the global land area, these CW maps are in the high end of the literature, together with recent estimates also recognizing the contribution of groundwater-driven wetlands  (Fan et al.
Higher wetland densities in the tropics compared with other datasets originate from the GDW contribution in regions with dense canopy and/or cloud cover. These conditions are tightly linked in the humid tropics, where wetlands have long been underrepresented  (Collins et al. , 2011; Melton et al. , 2013; Gumbricht et al. , 2017). The largest differences between the two proposed CW datasets are found in the boreal zones (including the two hotspots of the Prairie Pothole Region and East Siberian taiga), although the RFWs are the dominant components. This uncertainty corresponds to subsurface conditions (transmissivity) and might be reduced having a better and higher-resolution description of the permafrost extent, active layer depth, hydraulic conductivity or organic matter content. Another major feature of the two composite maps is the importance of small and scattered wetlands, as shown by the extent of wetlands outside the six hotspots (3.
8 % to 5. 2 % of the land area according to CW-WTD and CW-TCI15, respectively). This is yet another feature derived from the GDWs because these small wetlands are often difficult to detect using satellite imagery techniques, especially for the noninundated or ephemeral wetlands, with sizes that vary rapidly compared with the revisit period of the satellites. The resolution used in this work (∼ 500 m at the Equator) is sufficiently fine to detect many of these small wetlands, but a better delineation calls for the use of higher-resolution DEMs. By distinguishing the RFWs and GDWs, the proposed datasets eventually offer a simple wetland classification focused on their hydrologic functioning. Compared to classic wetland classifications, which are strongly based on floristic inventories and habitat typologies (e.
g. ,  Zoltai and Vitt, 1995; Finlayson et al. , 1999; Lehner and Döll, 2004; Herold et al. , 2015) , we separated areas where wet conditions at the surface are primarily driven by flooding, or GW inputs or both where the two classes intersect. Since the underlying principles and input datasets are globally valid, this classification is believed to be highly useful for land surface hydrological modeling. In particular, we intend to use it in the ORCHIDEE land surface model  (Krinner et al. , 2005; Ducharne et al. , 2017)  to describe the areas where GW convergence from the uplands to the lowlands can lead to high soil moisture, with a potential to enhance the local evapotranspiration and related land-atmosphere feedback (e. g. ,  Bierkens and van den Hurk, 2007; Maxwell et al. , 2007; Vergnes et al.
Acknowledgements. This research is a part of the  PhD project of Ardalan Tootchi , funded by  Région Ile de France  via the Réseau francilien de recherche sur le développement soutenable and by the  French Agence Nationale de la Recherche  ( ANR  grant  ANR-14-CE01-00181-01 ). Edited by: David Carlson Reviewed by: two anonymous referees.
We used a combination of keywords related to the type of exposure ("dobutamine stress echocardiography") and to the type of outcome ("atrial fibrillation"). The studies involved adults and could be of any design and any language. We excluded animal studies, ex vivo and toxicological studies, duplicates, summaries, commentaries, editorials, case reports, studies that did not evaluate AF incidence during DSE and studies without original data. Two independent reviewers (N. M. and H. M. ) screened all abstracts and titles to identify potentially eligible studies. The full text of these potentially eligible studies was then screened to determine the eligibility of the study for the review and meta-analysis. Disagreements regarding eligibility were resolved by consensus. Data extraction using a standardized form included a full description of the study characteristics.
Dobutamine infusion was immediately discontinued in case of AF. Duration of AF was less than one hour in most of patients (67. 5%) with spontaneous termination of AF (Table  2 ). For the 13 patients with duration of AF higher than one hour and after anticoagulation, pharmacological cardioversion (amiodarone or flecainide) was systematically attempted and only 7. 5% had persistent AF. For those patients, an electrical cardioversion was performed at distance and all patients had a recovery of sinus rhythm. Regarding the meta-analysis, the search strategy on the mentioned electronic databases, identified 199 citations to which we added 3 citations from reference lists of relevant studies and reviews. After screening titles, abstracts, 35 citations were considered potentially eligible and the full-text article was retrieved.
Cochrane Central Register of Controlled Trials and EBM Reviews-Cochrane Database of Systematic Reviews between 2005 and August 26, 2018. Moreover, this review was not exhaustive and since 2010 other studies have been published. Our meta-analysis complies with the preferred reporting items of PRISMA for systematic reviews and meta-analyses. A comprehensive search of several electronic databases (between 1948 and August 26, 2018) was conducted in EMBASE between 1988 and August 26, 2018; Ovid MEDLINE in-process and other non-indexed citations and Ovid MEDLINE between 1948 and August 26, 2018; and EBM Reviews-.
PM 2. 5 and O 3 peer-reviewed literature and select government-based certification documentation The US EPA sponsored a comprehensive review of select literature pertaining to both PM 2. 5 and O 3 sensor-based measurements and government-based air quality instrumentation certification requirements. This work has been summarized in detail in an US EPA Technical Report  (Williams et al. , 2018). Excerpts from the report were presented during the workshop by Ronald Williams (US EPA) and are summarized here. As part of the literature review, automated database searches were conducted using Compendex, Scopus, Web of Science, Digital libraries of Theses and Dissertations, Open Grey, OpenAIRE, Worldcat, US Government Publications, Defense Technical Information Center, and UN Digital Library. Computer-based searches using relevant sensorbased words/phrases revealed more than 20,000 citations from Jan 1, 2007 to Dec 31, 2017.
This time period was selected to ensure that only the timeliest literature would be examined relative to sensor performance targets. A systematic hand-curated review was conducted to reduce the quantity of citations to a meaningful number. Information from 56 titles was selected for this summary and included only the literature that provided the potential for informing the US EPA on 1) existing US and international performance standards, 2) identification of any non-regulatory technology performance standards, 3) discovery of the types of data identified in the reports, 4) data quality indicators (DQIs) and data quality statistics associated with low-cost sensor use, and 5) discovery of the air monitoring applications where specific sensor data quality indictors had been reported. Four broad categories of performance requirements were defined from this selection including spatio-temporal variability, comparisons, trends analysis, and decision-making.
An area of commonality among all the participants was expressed support for sensor performance targets. Additionally, that the performance targets should be at the "integrated device" level rather than at the OEM sensor level, given the system design including its packaging and data handling/processing algorithms, may modify the performance of OEM sensors. Performance targets were viewed as driving technology development, identifying well-performing devices from substandard technology, and aiding customers in their purchasing decisions. Several representatives noted their desire to see harmonization in sensor performance targets that may be developed in the US versus by international bodies (e. g. , CEN Technical Committee 264 Working Group 42). There was not uniformity among the representatives toward a tiered versus binary approach for sensor performance targets.
For example, precision was discussed in terms of percent error, ± a given concentration value, root mean square error, or coefficient of variability among others. Aggregation was performed only when a common metric was described (e. g. , a type of %) even if the means of calculating that measure might be variable. The range of performance attributes, and associated values, voluntarily shared by the SEs as necessary attributes were widely variable. Accuracy was considered a key performance attribute with acceptable ranges suggested to be 10-100% depending upon the purpose of the data collection. Median acceptable accuracy error values were calculated to be 25%. Precision was another performance attribute generating much discussion and in particular, the statistical term of merit. Regardless of the statistical term (e.
Thurston indicated that one of the most significant benefits of additional personal sensor measurements would be in support of future epidemiological assessments of the adverse health effects at the individual rather than overall population level. Cohen shared his perspective on the applicability of O 3 sensors for non-regulatory data collections from his experience with the BEACO 2 N study. He presented measurement findings associated with their lowcost sensor pod  (Kim et al. , 2018). While the BEACO 2 N study often had a primary focus on CO 2 observations, many of the same considerations could be applied to O 3 -based measurements. Published findings  (Turner et al. , 2016)  reported that use of a large number of low-cost sensors had the potential of outperforming a smaller number of more expensive monitors with respect to estimating near-road line source emission rates.
Tier 2 testing would ideally be conducted in real-world field settings to understand how a sensor performs outside of controlled laboratory conditions or other specific settings. As part of Tier 2 testing, sensors collocated with a reference instrument in the field can help identify bias, noise, interferences, and degradation over time. Seto noted that consumers want a device that is trusted and would like recommendations on which device to use and how good it will perform for their use case. Allen acknowledged that certification, while attractive, is complex, expensive, and difficult to implement, but would still be worth considering as a long-term goal  (Allen, 2018). He suggested constructing a more formalized testing and evaluation program that includes effective communication of the results to a wide range of end users with differing data quality needs.
Acknowledgements The authors acknowledge the contributions of  US EPA  workshop planning members ( Stacy Katz ,  Gail Robarge ,  Jenia McBrian ,  David Nash ) who contributed to establishing the goals of the workshop and worked to ensure its success. Stephan Sylvan  of  US EPA  is acknowledged for his presentation on historical certification considerations. We wish to thank the participants of the 2018  Air Sensor Performance Deliberation Workshop  and the subject matter experts who shared their perspectives on performance targets. Kelly Poole  and the  Environmental Council of States (ECOS)  are recognized for supporting Tribal and State air agency participation in the workshop. US EPA  staff including  Andrea Clements ,  Amara Holder ,  Stephen Reece ,  Stephen Feinberg , and  Maribel Colon  are thanked for their efforts in supporting the workshop webinar question/answer periods that contributed to the overall discussions.
In this work, we uniformly (re)analyze resolved (sub-)mm ALMA data for two dozen dipper discs in an effort to robustly infer the distribution of their outer disc inclinations. In Section 2 we present our sample and describe the datasets used in this work. We derive disc inclinations from the ALMA data in Section 3, then in Section 4 we construct the dipper disc inclination distribution and discuss its impact on our understanding of the dippers and inner disc misalignments. We conclude in Section 5 and suggest avenues for future work.
Ansdell et al. (2016b)  worked with citizen scientists to identify 25 dippers by eye and study 10 of them in detail. Hedges et al. (2018)  then used these 25 dippers to train a supervised machine learning algorithm with a random forest classifier to expand the sample to 95 dippers. Cody & Hillenbrand (2018)  employed their traditional statistics of periodicity and symmetry to categorize variable young stars in the K2/C2 dataset, identifying 94 dippers. Combining these samples results in 122 unique dippers. The issue with combining these samples is that significantly different criteria were used to identify the dippers, and sometimes the methods did not all agree. Therefore we consider all the dippers in the combined samples, then require at least three dips to be present in the K2/C2 light curves and apply the R dip > 5.
We use the former as they are associated with K2 C15 light curves shown in Figure  1. d These are the secondary components of the two binary discs resolved in the ALMA data (Section 3. 1). We then use data from our targeted ALMA programme as well as a search of the ALMA archive to identify those with resolved circumstellar discs (see Section 2. 3 for a description of the ALMA datasets used in this work, Section 3. 1 for the derivation of the disc inclinations, and Appendix C for a discussion of the dippers observed but not resolved by ALMA). We note that a general literature search did not return any dippers with disc inclinations derived only from pre-ALMA radio interferometers, and only one known dipper has an inclination derived only from scattered light (EPIC 204206295 or DoAr 28 with a modestly inclined disc of i d = 50 +15 -10 ;  Rich et al.
To correct for these effects, we use a modified Pixel Level Decorrelation (PLD) method to remove the roll motion noise while preserving intrinsic, astrophysical variability. First, we use the interact tool in the LIGHTKURVE package  (Lightkurve Collaboration et al. 2018)  to hand-select pixel apertures, which are customized to include as much of the target flux as possible while avoiding nearby contaminants. We then sum the flux within these custom apertures, which are typically a few Kepler pixels across (where one Kepler pixel is 4 ×4 ) to build the SAP light curves. Decorrelation matrices are then generated from: 1) pixel time-series of neighboring, quiet targets, which by nature strongly exhibit the K2 roll motion pattern; 2) fourth-order polynomials in time to capture extremely long term (>50-day) variability due to changes in spacecraft temperature and velocity aberration; and 3) two-dimensional, fourth-order polynomials of the point-spread function centroids in column and row (measured using LIGHTKURVE).