dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparisons between the groups for proportions will be assessed Missing data will be presented comprehensively and detailed in a table. After an assessment of whether the assumptions underlying the validity of the multiple imputation are plausible, these values will be handled with multiple imputation. For the primary outcome analysis, INR at hospital admission will be considered to have been performed at a comparable time in each patient and will be compared across the two arms with the Mann-Whitney U test. If the initial characteristics of patients are unbalanced between the two groups after randomization, an adjusted analysis will be considered with a regression method. Where appropriate, both adjusted and unadjusted results will be reported. The transfusion requirement will be compared by the number of blood products and plasma derivatives transfused after arrival at the hospital, including packed RBCs, platelet concentrates, fibrinogen, coagulation factors, and FFP.
sensitive than darker-coloured lakes and thus have greater changes in thermal structure  2, 23, 27, 28. Therefore, the variety of measures of water transparency, such as Secchi depth, and concentration of dissolved organic carbon (DOC), and chlorophyll-a, are likely to be important in understanding deepwater temperature trends because of their influence on vertical heat distribution. Here, we analysed a long-term, globally-expansive time series dataset (1970-2009) of summertime vertical lake temperature profiles from 102 lakes covering five continents and 18 countries. This study focused on lake thermal structure during the summer period when thermal stability was the strongest, and was described by five thermal metrics: surface water temperature, deepwater temperature, mean water column temperature, density difference, and thermocline depth.
The suite of lakes spans a wide range in location, elevation, water quality, trophic status, and morphometry, with high representation of globally-dominant small lakes  29  (45% with surface area ≤ 5 km 226 , 32% with maximum depth ≤ 20 m). We classified these lakes based on lake thermal region  30  to characterize the nature of this dataset and to analyse its global relevance for predicting trends in lake thermal structure. Lake thermal region is a global classification system based on seasonal dynamics of lake surface temperature, and thereby implicitly integrates other factors like location and elevation  30. Lake thermal region is not closely linked to air temperature trends or other terrestrial-based or ecoregion-derived classification systems, which indicates that the drivers of changes in lakes, including lake thermal structure, are likely different from those for terrestrial-or vegetation-based ecosystems.
Hence, lake thermal region is a unique method to characterize and compare lake-specific datasets from a globally-relevant perspective, and may be particularly useful for predicting changes in lake thermal structure for this dataset. We addressed two primary questions: (1) How has vertical lake thermal structure, particularly deepwater temperature and thermal stratification, changed in lakes across the world? (2) Does variation in lake thermal region, geography (e. g. , latitude, elevation), morphometry (e. g. , surface area, depth), or water quality (e. g. , Secchi depth, dissolved organic carbon, chlorophyll-a) explain observed temporal trends in lake vertical thermal structure? We predicted that lakes at high latitudes and high elevations would have the most rapid rates of surface and deepwater warming due to accelerated rates of climate change in these regions  31, 32.
Lake characterization by thermal region. There was a wide range in geography, morphometry, and water quality across the 102 lakes in this analysis (Table  1 ; see Supplementary Table  S1  online). Geographically, this dataset spanned 18 countries in five continents, with latitude ranging from 68. 9° N to 38. 8° S, longitude ranging from 159. 0° E to 176. 0° W, and elevation ranging from -210 to 1,882 m above sea level (asl). Very small to very large lakes were included, as surface area ranged from 0. 005 to 32,500 km  2  and maximum depth ranged from 2. 5 to 1,642 m. This dataset also included lakes from a broad array of trophic states and water transparencies, as indicated by the ranges in water quality variables, including Secchi depth (0. 5 to 31. 0 m), chlorophyll-a concentration (0. 1 to 60.
0 µg L -1 ), and DOC concentration (0. 1 to 18. 4 mg L -1 ). Seven of the nine thermal regions were represented in the dataset (Fig. 1a , Table  2 ). This dataset included no lakes from the Southern Hot or Southern Temperate thermal regions, which comprise a combined estimated 2. 3% of global lakes. Compared to the nearly 1. 5 million lakes in HydroLAKES, our dataset showed proportionally reasonable representation for most thermal regions, though with notable over-representation of Northern Temperate lakes and underrepresentation of Northern Frigid lakes (Table  2 ). Long-term trends in lake thermal structure. The lakes in this study had strong surface water warming trends and increases in density difference, but less consistent and highly variable trends in deepwater temperature (Figs. 1, 2, Table  3 ).
For the periods 1970-2009 and 1990-2009, surface water temperatures increased at median rates of + 0. 37 °C decade -1 (p < 0. 001) and + 0. 33 °C decade -1 (p < 0. 001), respectively (Fig. 2a ; Table  3 ). Similarly, density difference between surface and deeper waters increased significantly during both 1970-2009 (+ 0. 08 kg m -3 decade -1 , p < 0. 001) and 1990-2009 (+ 0. 06 kg m -3 decade -1 , p < 0. 001; Fig. 2d ). , and with world map data from the "ggplot2" R package  89. There was no relationship between surface water temperature trends and deepwater temperature trends across lakes (τ = 0. 09, p = 0. 12; Fig. 3a ), and there was no relationship between density difference trends and deepwater temperature trends across lakes (τ = -0. 08, p = 0. 17; Fig. 3b ). Drivers of changes in lake thermal structure.
Overall, random forest analysis using ten explanatory variables resulted in only a small percentage of the total variance explained across the thermal metric trends for 1990-2009. Trends in deepwater temperature only had 8. 4% of the total variance explained, but with several approximately equal predictor variables (Fig. 4a ). Deepwater temperature trends were best predicted by surface Table  2. Classification of lake thermal regions in this study compared to HydroLAKES database. Classification of thermal region  30  for the lakes in this dataset (n = 102), compared to the percentage of lakes in each from the globally-expansive HydroLAKES database (n = 1,427,148). area, thermal region, elevation, and DOC (Fig. 4a ). Small lakes were predicted to have decreasing deepwater temperatures, while in large lakes deepwater temperatures increased slowly (Fig.
Finally, although surface area, thermal region, elevation, and DOC were most important in predicting deepwater temperature trends, the total explanatory power was less than 10%. Hence, the drivers of deepwater temperature trends likely differ from those that drive changes in surface water temperatures, but cannot be clearly explained by thermal region or other standard lake characteristic information. The characterization of the lakes in this dataset using lake thermal region indicated good coverage of most major thermal regions, yet with clear geographic limitations. This dataset spanned a large range in geographic, morphometric, and water quality variables across the 102 lakes, and was proportionally well-represented in Northern Cool, Northern Warm, Northern Hot, Tropical Hot, and Southern Warm thermal regions compared to the estimated global distribution of lakes  30  (Table  2 ).
Our dataset was over-represented by Northern Temperate due to a random permutation compared to the most important variable, in order of decreasing importance. Variables marked with "X" had no increase in MSE and are statistically equivalent to random prediction. Random forest for thermocline depth resulted in 0% explanatory power, so no additional analysis was conducted. lakes in particular, with a higher density of lakes in North America and Europe, largely due to established longterm monitoring programs in these areas, which were necessary for our time series analysis of lake thermal responses. There were notable geographical gaps through much of Asia, Africa, and South America in particular, and few alpine lakes were included in this analysis, where long-term monitoring programs of full vertical thermal profiles are less common or only more recently established.
Two classifications were missing from this dataset, Southern Temperate and Southern Hot, which only account for 2. 3% of lakes worldwide  30. Perhaps most important to the responses of lake thermal structure was the under-representation of Northern Frigid lake thermal regions. Lakes in these regions are typically high-latitude systems that are experiencing the most rapid rates of air temperature warming due to polar amplification  31  and rapid changes in ice cover phenology  34, 35  that strongly influence lake thermal structure  36, 37. Under-representation of this thermal region likely limited our ability to assess the rates of change for high-latitude lakes in comparison with better-studied temperate systems. Improved representation of these geographic and thermal regions in particular may have resulted in more rapid changes than reported here for these northern systems.
In such cases, decreases in water transparency would result in surface water warming and "thermal shielding" leading to deepwater cooling  45  , producing increases in strength of thermal stratification and decreases in thermocline depth  2, 23, 33. In regions where drought is becoming more prevalent, enhanced evaporation-driven cooling of surface waters may decrease strength of stratification  62  , whereas increases in water transparency during drought would lead to increased light and heat penetration  61, 63  , resulting in largely opposite thermal responses to those from eutrophication or browning  33. Supplementing this dataset with time series of climate and water quality variables, specifically water transparency, in addition to more geographically-expansive temperature profile data particularly from underrepresented lake thermal regions such as Northern Frigid, Southern Temperate, and Southern Hot, would lead to an improved understanding of the drivers of whole-lake thermal structure at the global scale.
Methods Study sites. The 102 lakes included in this analysis were distributed across five continents and 18 countries, with a high density in North America and Europe (Fig. 1 ). Temperature profiles were generally recorded in the pelagic zone near the lake centre or site with the greatest depth. One of the limitations of studying deepwater temperature trends is that satellite data cannot be used as they assess surface or "skin" temperature alone  14, 15. Thus, here we use vertical temperature measurements recorded most often with a manual temperature probe and occasionally with an automated vertical profiling sensor. These measurements had a median of 1. 0 m depth increments across all lakes (range from < 0. 1 to 35. 5 m; see Supplementary Table  S1  online). The frequency of lake temperature profiles ranged from once per year up to sub-daily resolution, with a median of 9 profiles per year across all lakes (range from 1 to 703 profiles per year per lake; see Supplementary Table  S1  online).
Although we are aware that this approach has limitations for infrequently sampled or polymictic lakes, for well-stratified lakes it is as effective as using full summer data on thermal stratification and enables a more geographically extensive data set given that satellite data are not available for determining deepwater temperatures in lakes (Supplementary Fig. S1 online). Thermal metric calculations and temporal trend analysis. For each selected temperature profile, we calculated five metrics of vertical thermal structure that capture a range of patterns related to water column thermal changes and strength of stratification. These were defined as follows: (1) Surface water temperature (°C): the temperature reading at 2 m. The 2 m readings were chosen to minimize biasing from diel temperature oscillations and temporary surface thermoclines, especially in small lakes  75  , and to provide a metric relevant to habitat use and thermal exposure of organisms 76.
(2) Deepwater temperature (°C): the temperature reading at the deepest, consistently-sampled depth. (3) Mean water column temperature (°C): the average temperature from surface (0 m) through the deepwater temperature. The lack of complete bathymetric data prevented us from using volume-weighted whole-lake temperatures. (4) Density difference (kg m -3 ): the density difference between deep (#2) and surface (#1) waters (see Supplementary Fig. S2  and Table  S2  online). (  5 ) Seasonal thermocline depth (m): depth of the thermocline for the selected summer profile, where the maximum density difference > 0. 1 kg m -3 occurs between adjacent 0. 5 m depth layers  50  , using the R package "rLakeAnalyzer"  77. Using these thermal metrics for the selected summer temperature profiles, we defined two time periods to analyse: 1970-2009, which allowed us to assess patterns in lakes with 40 years of temperature sampling, and 1990-2009, which allowed us to include more lakes with a greater variety of morphometric and limnological characteristics and geographic coverage, albeit with a shorter time span of 20 years.
For each time period, three criteria were required for a lake to be included in the subsequent analyses: (1) At least one data point within 5 years of the start year (e. g. , 1970-1974 or 1990-1994). Thirty lakes met these criteria for the 1970-2009 time period, and 99 lakes met these criteria for the 1990-2009 time period (see Supplementary Table S1 online). All lakes except for three from the 1970-2009 period were also included in the 1990-2009 time period. Sample size varied slightly across thermal metrics since full water column metrics (all metrics except surface water temperature) could not be calculated for profiles that did not reach the necessary maximum sampling depth. For example, Lake Okeechobee (Florida, USA) only had surface temperature data available, and some very shallow lakes (e.
g. , Crystal Bog, Wisconsin, USA) did not have a consistently-sampled depth deeper than 2 m (surface water temperature depth), so in these cases, only the surface water temperature trends were calculated. For each lake and thermal metric, we calculated Sen's slope, estimating the median rate of linear change over time  78, 79  , for the periods 1970-2009 and 1990-2009 using the "wq" R package  80. We used one-sample Wilcoxon rank sum tests using all lakes' Sen's slopes as replicates to assess overall changes in each thermal metric against the null hypothesis that µ = 0, using a significance level of α = 0. 05. (1) RTR = (ρ deep -ρ surf )/(ρ 4 • C -ρ 5 • C ) Random forest analysis. Due to the high variability across lakes, particularly in the deepwater temperature trends, we sought to identify the key factors that influence changes in lake temperature and thermal structure.
We produced partial dependency plots for the most important predictor variables for each thermal metric (relative increase in MSE > 0. 8), and these plots indicate both the direction and nature of the relationship, including non-linear patterns. Partial dependency plots show the mean predicted response based on the random forest results versus the predictor variable of interest, while all other predictor variables are held constant  81, 86. Random forest analysis was conducted using the "randomForest" R package  87. All analyses were completed in R version 3. 5. 0 88 , and figures were created using the "ggplot2" R package  89.
Acknowledgements This work was conceived at the  Global Lake Ecological Observatory Network (GLEON) , and benefited from continued participation and travel support from  GLEON. This manuscript is dedicated to the late  Alon Rimmer  and  Karl Havens , who provided data and contributed to earlier versions of this manuscript. Funding in support of this work came from the following sources:  Belarus Republican Foundation for Fundamental Research ;  IGB Long-Term Research ; the  European Commission  within the  MANTEL  project; the  DFG  within the  LimnoScenES  project ( AD 91/22-1 ); OLA-IS,  AnaEE-France ,  INRAE of Thonon-les-Bains , CIPEL, SILA, CISALB;  Universidad del Valle de Guatemala; Archbold Biological Station ; the  Oklahoma Department of Wildlife Conservation , the  Oklahoma Water Resources Board , the  Grand River Dam Authority , the  US Army Corps of Engineers , and the  City of Tulsa ; the  Ministry of Business, Innovation, and Employment  ( UOW X1503 ); the  Natural Environment Research Council  of the UK; the IGB's  International Postdoctoral Fellowship ;  NSERC ,  Canada Foundation for Innovation ,  Canada Research Chairs, Province of Saskatchewan; University of Regina; Queen's University Belfast ;  Natural Environment Research Council ;  US-NSF, California Air Resources Board ,  NASA , and  US National Park Service ; the  Ministry of Higher Education and Research  (projects №  FZZE-2020-0026 ;  № FZZE-2020-0023 ) and.
They are also included in climate model simulations of the past millennium  (Jungclaus et al. , 2017)  in order to ensure that these runs mesh seamlessly with the historic simulations. However, the Land Use Harmonization dataset (LUH2:  Hurtt et al. , 2017)  only extends back to 850 CE, and thus scenarios of LULC changes are currently not included in the CMIP6 palaeoclimate simulations, including mid-Holocene simulations, that are used as a test of how well state-of-the-art climate models reproduce large climate changes. In this paper, we discuss how archaeological data will be used to improve global LULC scenarios for the Holocene. Given that there are large uncertainties associated with the primary data and further uncertainties may be introduced when this information is used to modify existing LULC scenarios, we outline a series of tests that will be used to evaluate whether the revised LULC scenarios are consistent with the changes implied by independent pollen-based reconstructions of land cover and whether they produce more realistic estimates of both carbon cycle and climate changes.
Finally, we present a protocol for implementing LULC in Earth system model simulations to be carried out in the cur-rent phase of the Palaeoclimate Modelling Intercomparison Project (PMIP:  Otto-Bleisner et al. , 2017; Kageyama et al. , 2018). However, the datasets and protocol will also be useful in later phases of other CMIP projects, including the Land Use Model Intercomparison Project (LUMIP) and the Land Surface, Snow and Soil Moisture Model Intercomparison Project (LS3MIP)  (Lawrence et al. , 2016; van den Hurk et al. , 2016).
g. Bartlein et al. , 2011) (Sect. 6). The parallel evolution of CO 2 and its isotopic composition (δ 13 C) can be used to derive the carbon balance of the terrestrial biosphere and the ocean separately  (Elsig et al. , 2009) ; in combination with estimates for other contributors to land carbon changes such as C sequestration by peat build-up, it provides a strong constraint on the evolution of LULC through time. An underprediction or overprediction of anthropogenic LULC-related CO 2 emissions during a specific interval results in consequences for the dynamics of the atmospheric greenhouse gas burden in subsequent times  (Stocker et al. , 2017)  (Sect. 7). Thus, these tests can be used to identify issues in the original archaeological datasets and/or the way these data were incorporated into the LULC scenarios that require further refinement.
Population dynamics from 14 C data Radiocarbon is the most routinely used absolute dating technique in archaeology, especially for the Holocene. Many thousands of radiocarbon dates are available from the archaeological literature. A number of regional and pan-regional initiatives are compiling these records through exhaustive surveys of the archaeological literature (e. g. the Canadian Archaeological Radiocarbon Database: https://www. canadianarchaeology. ca/, last access: 14 February 2020). Statistical approaches, such as summed probability distributions (SPDs), can then be used to infer past demographic fluctuations from these compilations (Fig. 3 ). This method assumes that the more people there were, the more remains of their various activities they left behind and that this is di-rectly reflected in the number of samples excavated and dated  (Rick, 1987 : Robinson et al.
, 2019). There are biases that could affect the expected one-to-one relationship between the number of people and the number of radiocarbon dates on archaeological material, including lack of uniform sampling through time and space caused by different archaeological research interests and traditions in different regions as well as increased preservation issues with increasing age, but these can be minimized by auditing the datasets. Assessment of the robustness of population reconstructions through time can be made statistically by comparing a null hypothesis of demographic growth constructed from an exponential fit to the data with the actual record of the number of dates through time  (Shennan et al. , 2013; Timpson et al. , 2014). Mathematical simulations show that the method is relatively robust for large sample sizes  (Williams, 2012).
Archaeological data about agricultural yields, combined with information from analogous contemporary cultures, historical information (e. g. Pongratz et al. , 2008) , and theoretical estimates of land use required to meet dietary and energy requirements (e. g. Hughes et al. , 2018) , can be used to provide regional estimates of per-capita land use for specific land use categories. LandCover6k will synthesize this information to allow regionally specific estimates of per-capita land use to be derived from the global land use maps. Information about the extent of grazing land is an important input for the development of revised LULC scenarios but from a carbon cycle modelling perspective, the amount of biomass removed by grazing is also a key parameter. Biomass loss varies not only with population size but also with the type of animal being reared  (Herrero et al.
However, LandCover6k has been collecting RPPs for China, south-east India, Cameroon, Brazil, and Argentina, and pollen-based land cover reconstructions will be available for sufficient parts of the tropics to allow the testing of the LULC scenarios. Another limita-tion of the REVEALS reconstructions is that RPP estimates are available for cultivated cereals but not for other cultivars or cropland weeds, so the LandCover6k pollen-based reconstructions will generally underestimate cropland cover  (Trondman et al. , 2015). It may also be possible to use alternative pollen-based reconstructions of land cover changes, such as the modern analogue approach (MAT: e. g. Tarasov et al. , 2007; Zanon et al. , 2018) , pseudo-biomization (e. g. Fyfe et al. , 2014) , or STEPPS  (Dawson et al. , 2016). While none of these methods require RPPs, MAT and STEPPS can only be applied in regions where the pollen datasets have dense coverage (such as Europe and North America), and pseudobiomization is affected by the non-linearity of the pollenvegetation relationship that the REVEALS approach is designed to remove.