dataesr/scientific-paragraphs-categorization huggingface dataset:
Interviews With the agreement of the physician in charge of the patient's care, a pharmacist researcher (AS or OD) informed the patient or the caregiver about the study and its objectives. Participants provided informed consent prior to the one-toone interview. A semistructured approach was used. The interviews took the form of a discussion about the patient's medications, the concept of medication review, the perception of the patient of risks and benefits of his/her medication, and what he/she would expect from a medication review. A topic guide was developed, pilot-tested, and used by both investigators (AS and OD). Interviews were recorded and then transcribed verbatim. Audio recordings of the semistructured interviews were analyzed using NVivo10 ® software. The analysis was conducted by two independent researchers (a physician and a psychologist) using an interpretative approach.
Publish your work in this journal Submit your manuscript here: http://www. dovepress. com/clinical-interventions-in-aging-journal Clinical Interventions in Aging is an international, peer-reviewed journal focusing on evidence-based reports on the value or lack thereof of treatments intended to prevent or delay the onset of maladaptive correlates of aging in human beings. This journal is indexed on PubMed Central, MedLine, CAS, Scopus and the Elsevier Bibliographic databases. The manuscript management system is completely online and includes a very quick and fair peer-review system, which is all easy to use. Visit http://www. dovepress. com/testimonials. php to read real quotes from published authors.
Acknowledgments This work is part of the project " OPERAM: OPtimising thERapy to prevent Avoidable hospital admissions in the Multimorbid elderly " supported by the  European Union 's  Horizon 2020 research and innovation program  under the grant agreement No  6342388 , and by the  Swiss State Secretariat for Education, Research and Innovation  under contract number  15. 0137. The opinions expressed and arguments employed herein are those of the authors and do not necessarily reflect the official views of the  European Commission  and the  Swiss government. We would like to thank  Dr Lisa G Pont  and  Prof Johanna I. Westbrook , who participated in the systematic review, for their invaluable assistance. We would also like to thank  Dr Aoife Waters ,  Dr Liz Gargon , and  Prof Paul Jansen  for their invaluable help and useful advice.
Finally we provide guidance on the use of the available LIG climate compilations for future model-data comparison exercises in the framework of the upcoming CMIP5/PMIP4 127 ka experiments. We do not recommend the use of LIG peak warmth-centered syntheses. Instead we promote the use of the most recent syntheses that are based on coherent chronologies between paleoclimatic records and provide spatio-temporal reconstruction of the LIG climate. In particular, we recommend using our new 127 ka data-based time slice in model-data comparison studies with a focus on the high-latitude climate. Key words: Last Interglacial, 127 ka surface temperature time slice, CMIP6/PMIP4 Tier 1 and Tier 2 simulations, quantitative uncertainty estimates attached to relative dating and temperature reconstruction methods.
, 2015). Second, they consist of a compilation of peak warmth values not occurring necessarily at the same time. Indeed, there is much evidence that the LIG warming was not synchronous globally (e. g. Bauch and Erlenkeuser, 2008; Cortese et al. , 2007; NEEM-community-members, 2013; Govin et al. , 2012; Masson-Delmotte et al. , 2010). Thus, the  Turney and Jones (2010)  and  McKay et al. (2011)  syntheses do not represent a climate state prevailing at any specific time periods across the LIG, but instead a virtual image on the LIG peak warmth across the globe. As a result and despite their global geographical extent, the  Turney and Jones (2010)  and  McKay et al. (2011)  datasets represent inadequate benchmarks for the upcoming lig127k simulation and associated sensitivity experiments. We overcame the difficulty of harmonizing chronologies of marine and ice records from different hemispheres using a strategy based on climato-stratigraphic alignments  (Capron et al.
(2014)  synthesis to assist the assessment of the CMIP5/PMIP4 LIG Tier 1 and Tier 2 simulations in the high-latitude regions. After a brief description of the materials and methods (Section 2), we describe the main climatic features highlighted in the 127 ka time slice and differences with the ones existing for 130 and 125 ka (section 3). Finally, we compare our new 127 ka time slice with other existing LIG data syntheses, evaluating their strengths and limitations. We also discuss the validity of such a data time slice at 127 ka in the context of the upcoming modeling exercise (Section 4).
For each marine core location, we extracted the preindustrial SST from the HadISST dataset for the 1870-1899 interval  (Rayner et al. , 2003). For our marine core locations, the gridded NODC WOA98 summer SST dataset (representative of modern-day) is on average 0. 1°C warmer than the gridded 1870-1899 HadISST dataset in the North Atlantic region and 0. 2°C cooler in the Southern Ocean (see Table  S1 ). Note that HadISST is a statistically blended and interpolated dataset of different measurements, representing optimal SST spatial distributions. Uncertainties associated to the HadISST are not currently available  (Rayner et al. , 2003). These HadISST values at the marine core sites cannot be tested against in-situ preindustrial SST reconstructions since the paleorecords selected for the LIG synthesis do not have a sufficient resolution to correctly establish the pre-industrial climate.
Yet, the HadISST is shown to be consistent with other datasets across overlapping time span, therefore its uncertainties are likely small  (Rayner et al. , 2003) , especially in comparison to the typical error related to the SST reconstructions included in our study. The preindustrial estimate (-28. 8 ±0. 7°C) of the Greenland NEEM ice core site is based on borehole temperature measurements  (Masson-Delmotte et al. , 2015). In the absence of such reconstructions for the Antarctic sites, the estimates corresponding to the 1870-1899 time interval rely on water isotopic profiles, they are similar to modern instrumental ones within less than 1°C except for EDML (less than 2°C; A.
Considering that  Capron et al. (2014)  and  Hoffman et al. (2014)  use the exact same original SST datasets on a depth scale for these selected sites, we interpret the temperature differences recorded at 127 ka and ranging between 0. 1 to 2. 0 °C for the different sites in Table  3  as resulting from three main factors. (1)  Capron et al. (2014)  use AICC2012  (Bazin et al. , 2012; Veres et al. , 2012)  as the reference chronology while  Hoffman et al. (2017)  use the SpeleoAge time scale which results from the adjustment of the ice core EDC3 timescale using radiometric dates from Chinese speleothems  (Barker et al. , 2011 ). An offset of about 1 ka is observed between the two time scales around 127 ka, it increases through time reaching up to 3. 4 ka around 115 ka (Figure  4 ). (  2 ) The definition of the tie points and associated relative uncertainty, which influence the determination of the final age model and thus the resulting 127 ka temperature values, are different.
(3) Despite a similar approach based on a Monte-Carlo analysis with 1000 age model realizations, the two studies do not use the same method of calculation to deduce the age model for each site, as well as quantitative estimates of associated uncertainties. The methodology of  Hoffman et al. (2017)  is based on Bayesian statistics  (Haslett and Parnell, 2008)  and results in a greater smoothing of the SST changes on an age scale compared to those inferred with our methodology based on linear interpolation between tie-points. This is illustrated in Figure  4  with a comparison of the resulting dated surface temperature records for the North Atlantic site EW9302-8JPC  (Oppo et al. , 1997; Oppo et al. , 2001)  that show an offset of 2°C at 127 ka. This exercise of comparison shows how critical age models, and the way they are defined, are in LIG data compilations.
Because (1) air temperature estimates represent precipitation-weighed and annual signals for Greenland and Antarctica respectively, and (2) SST records are mostly interpreted as summer signals, we cannot propose a realistic 127 ka high latitude average surface temperature encompassing all records in our data synthesis. Instead, we propose regional temperature anomalies for the North Atlantic, the Southern Ocean (summer averages) and Antarctica (annual average) at 125, 127 and 130 ka (Table  1 ) based on records on coherent chronologies and relative to preindustrial. We infer from the  Capron et al. (2014)  dataset that North Atlantic and Southern Ocean summer averages were +1. 1°C (SE: 0. 7°C, based on 14 records) and +1. 8°C (SE: 0. 8°C; based on 15 records) respectively at 127 ka. Following the same methodology, we infer also annual and summer North Atlantic and Southern Ocean averages based on the sites poleward 40°N and 40°S included in the  Hoffman et al.
(2017)  datasets. 127 ka North Atlantic and Southern Ocean summer averages were +1. 9°C (SE: 1. 7°C, based on nine records) and +1. 6°C (SE: 0. 9°C, based on seven records) respectively while North Atlantic and Southern Ocean annual averages were -0. 2°C (SE: 1. 4°C, based on nine records) and +2. 7°C (SE: 1. 0°C, based on 12 records, see footnote of Table  1  regarding this annual average). Despite existing surface temperature differences observed at 127 ka for a given site included in both compilations (c. f. section 4. 1. 2, Table  2 ), the North Atlantic and Southern Ocean regional summer averages from the two compilations based on different methodologies to infer a common chronological framework are overall consistent within their stated uncertainties. Combining the annual and summer averages represents useful quantitative information to evaluate (1) the seasonality of climate models in the high-latitude regions and (2) the sensitivity to the high-latitude summer warmth of the polar ice sheets whose melting caused a 6-9m global sea level rise.
We provide an in-depth evaluation of existing LIG climate syntheses and guidance for using them to evaluate climate model simulations. Due to their chronological limitations and their focus on the LIG peak warmth only, we do not recommend the use of the syntheses of  Turney and Jones (2010)  and  McKay et al. (2011)  as benchmarks for the CMIP6/PMIP4 127 ka simulations performed in the high-latitude regions. We recommend instead using the new 127 ka surface temperature dataset. Associated with summer SST reconstructions, it will be particularly useful to evaluate data-model temperature differences seasonally in the context of a large summer insolation forcing in the North Hemisphere. Site-specific estimates provide detailed information about the spatial structure of the changes and the regional averages provide first-order estimates of mean responses at 127 ka.
The  Turney and Jones (2010)  compilation is the only one including continental records. Thus, the fact that it does not represent any specific nor realistic time window should be kept in mind when using it for model-data comparison exercises over land or when using as a benchmark its global annual surface temperature estimate of +1. 5 ±0. 1°C relative to the  1961 -1990 interval. Hoffmann et al. (2017) )  dataset represents currently the only global ocean synthesis associated with harmonized chronologies extending to the low latitude annual ocean surface temperatures and it should be favored over the  McKay et al. (2011)  synthesis when investigating LIG SST changes at a global scale. It is common practice to evaluate simulations of present-day climate to multiple observation datasets considered independently because of different blending methods and assimilation systems.
In the same way and because of the use of different reference chronologies and methodologies to infer temporal surface temperature changes, the  Capron et al. (2014)  and  Hoffman et al. (2017)  datasets should not be combined as such but, instead, treated as independent data benchmarks. Overall, our critical evaluation of available climate syntheses for benchmarking upcoming CMIP6/PMIP4 LIG simulations at 127 ka calls for the urgent need to develop millennial-scale transient climate syntheses of global extent, integrating ice, marine and continental records in a coherent temporal framework accounting for age and tracer uncertainties. In addition, future syntheses should also extend temperature synthesis to additional parameters such as deep ocean circulation changes and sea ice extent. The LIG is not a direct analogue to future climate due to differences in the primary forcing of the warming i.
The vertical yellow line indicates 127 ka, the time interval chosen to run the coordinated CMIP6/PMIP4 LIG Tier 1 and Tier 2 simulations. The grey shading indicates the 126- Values for the 130 and 125 ka SST anomalies and associated 2σ errors are identical to the ones provided by  Capron et al. (2014)  but some differences exist regarding the 130 and 125 ka surface air temperature estimates and associated uncertainties for reasons described in Section 2. 2014 ) and in  Hoffman et al. (2014). The North Atlantic core EW9302-8JPC SST record is used as a case study  (Oppo et al. , 1997; Oppo et al. , 2001). Note that both compilations use the same raw SST dataset on a depth scale. a) The black curve with grey diamonds represents the raw EW9302-8JPC SST data displayed on AICC2012  (Bazin et al.
b) EPICA Dome C water isotopic profile  (Jouzel et al. , 2007)  displayed on the AICC2012 timescale in red and on the SpeleoAge timescale in blue. Horizonal grey arrows indicate the age difference in specific events between the two age scales. An age difference of about 1. 4 ka prevails between the two timescales at 127 ka. Age differences around 120 and 115 ka illustrate that the choice of a reference timescale becomes increasingly critical at the end of the LIG and over the glacial inception when an offset of more than 3 ka is observed between the two time scales around 115 ka. In both panels, the vertical black line indicates the 127 ka time interval. 1 Table  1. 130, 127 and 125 ka regional surface temperature changes deduced from the  Capron et al. (2014)  datasets and 129, 127 and 125 ka regional surface temperature changes deduced from the  Hoffman et al.
(2017)  datasets. Area-weighted average summer (or annual) SST values, calculated for the North Atlantic and the Southern Ocean sectors and area-weighted annual air temperature values calculated for the Antarctic sector are indicated in bold. For each case, the associated standard error (2σ) ranging 0. 5 to 1. 7°C is given in italics. The number of records considered for the calculation for each sector and each time slice is indicated in brackets. A value and associated mean squared error is also given for regional surface temperature differences between the (1) 127 and 130 ka and (2) 125 and 127 ka from the  Capron et al. (2014)    ** The Southern Ocean Annual SST average is anomalously high considering the Southern Ocean summer average (1. 8 ±0. 9°C). This is likely due to high SST anomalies calculated for cores ODP-1089, MD97-2121 and MD97-2120.
anomaly was then deduced by using the HadlSST1. 1 1870-1889 values provided in the supplementary material of Hoffman et al. (2017). Overall, a comparable number of high-latitude SST records is included in Hoffman et al. (2017)  and Capron et al. (2014)  but the latter includes in addition polar surface air temperature records from ice cores. The two SST datasets are somehow complementary since Capron et al. (2014)  provide mostly summer SST records (40 summer and two annual SST reconstructions) and Hoffman et al. (2017) gather 17 summer and 22 annual SST records. However differences in methodologies to define the common temporal framework and to estimate surface temperature changes prevent us to propose a 127 ka time slice combining directly both syntheses (see further discussion below). The two syntheses have 12 summer SST records in common.
Acknowledgments: We are grateful to members of the  PAGES/PMIP Working Group on Quaternary Interglacials (QUIGS)  and we thank in particular all participants of the Warm Extremes Workshop (November, 9-12 th 2015, Cambridge, UK) for stimulating discussions. We also thank  Anaïs Orsi  for useful discussions regarding ice core temperature reconstructions. This work is a contribution to the  PAGES/PMIP Working Group on Quaternary Interglacials (QUIGS). E. C. is funded by the  European Union 's  Seventh Framework Programme  for research and innovation under the  Marie Skłodowska-Curie  grant agreement no  600207. B. L. O-B is supported by the  U. S. National Science Foundation (NSF ) sponsorship of NCAR. R. F. acknowledges the funding of the  NSF Arctic System Science. E. W. W. is supported by the  Royal Society.
datasets. Details on the calculation methodology are provided in the Supplementary Material. Note that only marine sediment core records for which SST records are interpreted as summer signals are included in calculations from theCapron et 130 a Mean 129 ka Mean 127 ka Mean 125 ka Mean 127-130 ka difference 125-127 ka difference Sector Capron et al. 2014 * N. Atl. Summer SST -4. 2 ± 0. 8 (13) N/A 1. 1 ± 0. 7 (14) 1. 2 ± 0. 7 (14) 5. 3 ± 1. 0 0. 1 ± 1. 0 S. Ocean Summer SST 1. 8 ± 0. 9 (15) N/A 1. 8 ± 0. 8 (15) 1. 6 ± 0. 8 (15) 0. 1 ± 1. 2 -0. 3 ± 1. 2 Ant. Ann. Air Temp. 1. 9 ± 1. 4 (4) N/A 2. 2 ± 1. 4 (4) 1. 3 ± 1. 4 (4) 0. 3 ± 2. 0 -0. 9 ± 2. 0 Hoffman et al. 2017 N. Atl. Summer SST N/A -0. 8 ± 1. 7 (9) 1. 9 ± 1. 7 (9) 2. 7 ± 1. 2 (9) N/A N/A N. Atl. Annual SST N/A -3. 0 ± 1. 6 (9) -0. 2 ± 1.
For example, pan-tropical maps of the C stored in AGB have been produced using spaceborne remote sensing data at 1 km scale for the early 2000s  (Saatchi et al. , 2011)  and at 500 m scale for 2007  (Baccini et al. , 2012) , while similar maps were produced for the temperate and boreal regions of the northern hemisphere in 2010  (Thurner et al. , 2014). The two pantropical AGB maps  (Baccini et al. , 2012; Saatchi et al. , 2011)  used similar input data layers, and are principally based on canopy height data derived from the LiDAR dataset acquired between 2003 and 2009 by the Geoscience Laser Altimeter System (GLAS) sensor onboard the National Aeronautics and Space Administration (NASA) Ice, Cloud and Elevation Satellite (ICESat). However, these studies differ in the methods used to derive height from the GLAS data, the ground datasets used for calibration and the spatial modelling methodology; as a result, there are significant regional differences between them, especially across the tropical rainforests (central Amazon, Congo basin, Papua New Guinea), savanna woodlands in Africa (i.
It was against this background that BIOMASS was selected in 2013 as the 7th European Space Agency (ESA) Earth Explorer mission, with the aim of providing accurate estimates of the distribution of AGB in the world's forests at a spatial scale of ~200 m. The Report for Selection  (ESA, 2012)  showed clearly that the mission had to be based on a Pband Synthetic Aperture Radar (SAR), for which permission had been granted by the International Telecommunication Union (ITU) as a secondary allocation in the 432-438 MHz frequency band at the World Radiocommunication Conference in 2003  (ITU, 2004). Furthermore, the science case on which BIOMASS was selected was especially built on its ability to measure AGB within dense tropical forests, which, despite having the highest total forest C stock values (and mean forest AGB density), have minimal coverage by ground data, in contrast to the extensive ground data existing for temperate and boreal latitudes (driven largely by the needs of commercial forestry)  (Schimel et al.
Data and methods The impact of SOTR operations on BIOMASS objectives will be evaluated by combining the information about the location and area of influence of SOTR stations with that representing the extent of global forest cover and corresponding AGB C stocks. The areas affected and unaffected by these stations will be reported at global, ecological and country scale. Additionally, the impact of the SOTR network in terms of loss of representativeness of AGB information for large-scale ESM modelling will be assessed by identifying the magnitude and location of the occluded areas in a climate space defined by precipitation and temperature. We provide below a critical description of the datasets used for this purpose, including additional information about some methodological steps that are specific to these datasets.
To support our analysis, forest cover maps for 2005 and 2010 were generated from the baseline tree cover map of 2000 using the yearly loss information. Forest gain in  Hansen et al. (2013)  was not considered as separation at annual time steps is not currently possible. These two reference years were chosen because: i) the global AGB map used in this study  (Saatchi et al. , unpublished results ) is representative of AGB density around 2005; ii) one of the most recent FAO GFRA (FAO, 2010) contemporaneous with the tree cover dataset of  Hansen et al. (2013)  is from 2010. We followed a conservative approach in defining forest: only land with tree cover not b 25% was considered as forest, whereas FAO's GFRA uses a threshold of 10%. The full resolution (30 m) tree cover dataset from 2000 was first converted into a forest/ non-forest map using the 25% tree cover threshold and then updated to 2005 and 2010 using the data on yearly loss.
Above-ground biomass (AGB) Two pan-tropical AGB maps  (Baccini et al. , 2012; Saatchi et al. , 2011)  have recently been developed at grid scales of 1 km and 500 m respectively. Both use similar input data layers, and are principally driven by the same (though re-analyzed) spaceborne LiDAR dataset acquired by ICESat GLAS between 2003 and 2009. However, they use different ground datasets for calibration and different spatial modelling methodologies. As a result, there are significant regional differences between them, which tend to decrease when AGB estimates are aggregated to country or biome scale  (Mitchard et al. , 2013). The AGB and C calculations in this paper are based on an updated global version of the  Saatchi et al. (2011)  map  (Saatchi et al. , unpublished results). The global map is developed by making use of the ICESat GLAS measurements globally and existing regional algorithms for the global ecological zones (Fig.
, 2006). We added AGB and BGB values and aggregated the biomass map to 1 km in order to reduce the uncertainty at finer spatial resolution. A factor of 0. 47 was used to convert forest woody biomass to C content  (McGroddy et al. , 2004). The 1-km global data set is currently available from the JPL Carbon Monitoring System website (https://cmsun. jpl. nasa. gov).
Climate datasets A different perspective on the effect of loss of coverage due to SOTR operations, particularly relevant to large scale C cycle calculations and ESMs, is given by examining how forest biomass is distributed in climate space and how this distribution changes when the area affected by SOTR operations is removed. In this analysis we used the sum of AGB and below-ground biomass (BGB) estimates from  Saatchi et al. (unpublished results ) to obtain the mean vegetation C (cVeg) for all 0. 5°land grid cells having a minimum tree cover of 25%  (Hansen et al. , 2013)  and cVeg N 5 Mg C ha -1. The air temperature and rainfall data at 0. 5°s patial resolution for the period 1982-2005  (Carvalhais et al. , 2014)  were derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis products  (Dee et al.