dataesr/scientific-paragraphs-categorization huggingface dataset:
From this database, we have derived 3 smaller subsets: For each of these reduced data sets, we ran four of the previously tested VQIs. The objective is to ascertain that the best performing metric can be easily determined from a reasonably small subjective data set. Fig. 12  shows, as a radar plot, the Pearson correlations between 15 randomly selected observers and four selected VQIs. We present the correlations values for the full size database (756 images) as well as for the 3 reduced versions. It can be observed that among the 4 tested metrics, the VQIs ranking seem to be globally preserved. However, the predicted scores from most VQIs seem to increase when the data set dimensions decreases. In our example this is particularly true for the VIF metric which appears to provide misleading predictions on the smaller data sets for the 265 SEnc method.
A dataset for the development of recognition indices While we were not successful in creating a new recognition index (this was also not the aim of this work), we have shown that objective metrics based on the dataset we collected, and subsequently share with the research community, already  show an improvement over existing VQIs. The dataset will be provided to the research community with the purpose of facilitating further research and the development of recognition indices. Finally, in case of unseen encryption types, we have provided guidelines on the acquisition environment and number of participants as well as the setup of the experiment to facilitate extension of the dataset in a meaningful way.
Statistical analysis To investigate the time series data generated by this approach, we use change-point modelling  (Gallagher et al. , 2011). This allows for the inference of statistically significant shifts in the dataset, meaning we can better investigate the drivers of change. To investigate the impact of past climate and moisture variability on the CARs of individual sites during discreet periods, we calculate average CARs for three periods of interest with respect to both climate and human impact: the Roman Warm Period (RWP; 2200-1550 years BP), the Medieval Warm Period (MWP; 950-1250 CE), and the Little Ice Age (LIA; 1300-1850 CE). We also calculated the apparent recent rate of carbon accumulation (RERCA) for the period from 50 years BP to present (Table  1 ). However, the RERCA values should be treated with caution because CAR values in the uppermost layers of peat may vary due to the exponential nature of peat decay  (Clymo et al.
Composite CAR record and data-model comparison To interpret the effect of past hydroclimatic drivers on the Romanian Carpathian mountain peatlands, we synthesize all datasets, resulting in a representative record of CAR across the middle and late Holocene in the region (Fig. 3 ). To achieve this, for each individual site, we bin all data at 50year intervals. For these bins, we consider the 25 years either side of the bin age (i. e. the 0 years BP bin covers the period 26 to -25 years BP). To allow for comparison across sites, we convert these raw binned CAR data into z scores. We then calculate the mean and standard deviation values for each of the bins, for all studied sites. This approach allows for the interpretation of large-scale changes in CAR for a region across a long time period. As individual CARs are highly variable, with numerous stochastic and site-specific forcing factors, averaging multiple records provides insights into broad-scale factors controlling regional CARs  (Loisel et al.
, 2021; Marlon et al. , 2016). To compare our composite CAR output to modelled palaeoclimatic indices, we use data from PaleoView  (Fordham et al. , 2017). We extracted six parameters related to past precipitation and temperature variability from PaleoView, with each temperature variable expressed as a z score relative to recent (i. e. present day set as 1975 CE, or -25 years BP, in the model). For each variable, we use the mean area extracted from the region 40-47. 5 • N, 17. 5-27. 5 • E, which covers all sites in this study (Fig. 1 ). To investigate the effect of other potential drivers, we compare the composite output to three measures of human impact and land use (population, area of pasture, and area of cropland) derived from the History Database of the Global En-vironment (HYDE) dataset  (Goldewijk et al.
, 2017). Due to the nature of the dataset, we use the "Central Europe" region, as it contains both Romania and Serbia  (Goldewijk et al. , 2017). We also explore an estimate of the intensity of land use in the region, from  Kaplan et al. (2011) , which represents a proportion of the 5 • grid square (centred on 46 • N, 24 • E) utilized for any type of human activity on the landscape. To investigate the impact of dust deposition, we used two regionally representative reconstructions of dust deposition through the Holocene, from MOH  (Longman et    2017b) and TM  (Panait et al. , 2019). Furthermore, to explore the impact of biomass burning on the record, we compare it with the compilation of eastern European charcoal deposition from  Feurdean et al. (2020) , from which we extract the boreal (BOR) record, as this covers all sites in this study.
Data-model comparison One way in which potential drivers of change in peat accumulation may be elucidated is by comparing our data to model reconstructions of climatic variables, such as temperature and precipitation, and investigating their relationship with the observed shifts in CAR (Fig. 4 ). Here, we compare the results of our composite record (binned at a 50-year resolution) to a number of climatic indices extracted at 50year time steps from modelling reconstructions of the last 5000 years  (Fordham et al. , 2017). When comparing the full datasets, several statistically significant correlations are revealed (Table  3 , Fig. 4 ). These include both temperature and precipitation seasonality and diurnal and annual temperature range (Table  3 ). Strong nega-tive correlations between both precipitation and temperature seasonality and CARs indicate that CARs decrease as seasonality increases.
, 2016). Another factor in the control of CAR by dust deposition is the peat type, with ombrotrophic bogs considered very nutrient poor and, generally, phosphorous and nitrogen limited  (Damman, 1986; Ratcliffe et al. , 2020; Wang and Moore, 2014). This may explain the lack of strong correlation between our CAR composite and two regionally representative dust flux records (Table  4 ). We showed that both dust input and variability in hydroclimate may have played a role in controlling CARs in the study region, but we hypothesize that human impact may have become the major controlling factor after 3000 years BP. To investigate this, we compare the CAR composite record with several land-use-and human-impact-related proxies from the HYDE dataset (Fig. 6 ). We focus on the last 2000 years of the record due to data availability, as only a single data point for every 1000 years is available from the HYDE dataset prior to 2000 years BP.
However, these data are sufficient to indicate the strong positive correlations of population and land use with CARs in central-eastern Europe (Table  4 ). This is further supported by the comparison with land use as reconstructed by  Kaplan et al. (2011)  and by the strong positive correlation between the two datasets (Fig. 6 , Table  4 ). To supplement this, we also carry out a comparison with a synthesis of eastern European biomass burning  (Feurdean et al. , 2020). In the studied period, it appears that biomass burning was primarily controlled by human-driven tree felling and burning; thus, it may be used to estimate the levels of land exploitation  (Feurdean et al. , 2020). There are strong positive correlations between CARs and biomass burning in the last 2000 years (Fig. 6 , Table  4 ).
This exercise clearly indicates the covariance of dust and climatic forcing factors (especially precipitation) when considering the whole record. However, for the periods in which we infer local erosion and dust supply stimulating peat growth, there are either very weak or not statistically significant correlations between climate and dust (Table  S3 ). Such a finding indicates the disconnect between climate and dust flux over the last 2000 years and supports our assertion of local dust drivers in this period. Clim This data comparison exercise clearly indicates the complexity of factors controlling mountain peat CARs as well as the fact that prediction of the behaviour of the peatland carbon sink is a challenge. Considering climate indices, it appears that there is a relationship between mean temperature and CAR and, potentially, between mean temperature and the availability of moisture during the growing season (Table  4 ).
Conclusions Using nine peatbog records from the Carpathians, we compiled a detailed record of changing carbon accumulation rates (CARs) through the Holocene. Our CAR data indicate that bogs located in the Carpathian Mountains are potentially very efficient carbon sinks, with long-term apparent CAR values that are amongst the highest in the published literature and that are comparable to high mountain bogs in South America. As a result, the peatland carbon sink in the Carpathians and throughout central-eastern Europe may be large, despite the small area of peatland coverage. Our work suggests that mountain bogs should be considered in future estimates of the peatland carbon sink and that further studies are needed to refine estimates of carbon accumulation in such environments. From our reconstructions, CARs varied throughout the Holocene, with high rates recorded in the early Holocene (10 000-7000 years BP), followed by a slow decline, culminating in CAR nadir around 4000 years BP.
This would help to determine exactly how important mountain bogs are in the regional carbon balance, and, when combined with management and monitoring exercises, may help to mitigate the worst of the impacts of anthropogenic climate change. Data availability. All data generated in the course of this study are available from the following Figshare links: https://doi. org/10. 6084/m9. figshare. 16930987. v1 (Longman, 2021b)  and https://doi. org/10. 6084/m9. figshare. 16930867. v1 (Longman, 2021a). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/cp-17-2633-2021-supplement. Author contributions. JL, DV, AH, VE, and WF were responsible for conceptualizing the study. JL, DV, and WF developed the methodology. JL, DV, VE, AH, WF, and DP undertook the formal analysis.
The authors are indebted to Slobodan B. Marković, Nemanja Tomić, Igor Obreht, and Szabolcs Kelemen for fieldwork support. The authors acknowledge the financial support from the Romanian Ministry of Education and Research. We are grateful for the insightful comments from Joshua Ratcliffe and Nicole Sanderson, who helped improve the paper. Financial support. This research has been supported by the Romanian Ministry of Education and Research, CNCS -UEFISCDI (grant no. 85 PN-III-P4-ID-PCE-2020-0914). Clim. Past, 17, 2633-2652, 2021 https://doi. org/10. 5194/cp-17-2633-2021.
Acknowledgements. The authors are indebted to Slobodan B. Marković, Nemanja Tomić, Igor Obreht, and Szabolcs Kelemen for fieldwork support. The authors acknowledge the financial support from the Romanian Ministry of Education and Research. We are grateful for the insightful comments from Joshua Ratcliffe and Nicole Sanderson, who helped improve the paper. Financial support. This research has been supported by the Romanian Ministry of Education and Research, CNCS -UEFISCDI (grant no. 85 PN-III-P4-ID-PCE-2020-0914). Clim. Past, 17, 2633-2652, 2021 https://doi. org/10. 5194/cp-17-2633-2021.
DNA extraction, amplification and sequencing Genomic DNA was extracted from dried leaves and stem samples (~100 mg) using NucleoMag Plant kit ® (Macherey nagel ® ) according to the manufacturer's protocol. The quantity and quality of DNA extracts were quantified and verified by spectroscopy using a SimpliNano ® microvolume spectrophotometer (GEe HealthCare ® ). Two barcodes regions were chosen in the database "BOLD Systems v3" and one barcode primer was designed using primer3plus software (Table  1 )  [39, 40]. rbcl and trnH-psbA primers have ambiguous nucleotide bases because they allow the identification of a large number of plant species. All three barcodes were amplified by PCR (polymerase chain reaction). The PCR conditions were 95°C for 10 min, followed by 35 cycles at 95°C for 30 s, primer melting temperature depending on the primers for 30 s and 72°C for 1 min, with a final incubation at 72°C for 7 min.
Chemical marker identification and selection The ethanol extract constituents of the lavender samples were determined by UHPLC-UV-MS. EtOH (3:1) was selected as extraction solvent because it provides the highest level of non-target compounds. Chromatogram analysis detected 99 major compounds present in the samples (Data S1). Comparison of chromatograms of the different samples (triplicate analysis for each compound) allowed to identify 7 compounds with significant peak area values in at least one of lavender analysed samples (Figure  1 ). Four compounds belonged to the hydroxycinnamic acid family, compound n°8 (coumaric acid hexoside), compound n°14 (ferulic acid hexoside), compound n°21 (a molecule derived from coumaric acid) and compound n°53 (rosmarinic acid). Two others compounds belonged to the family of phenylpropanes, compound n°15 (glucoside of hydroxycinnamic acid) and compound n°16 (derivative from cinnamic acid).
Chemical profile Figure  1  shows the chromatography profiles of the samples. The set of molecules identified is presented in Data S1. Among the 99 compounds measured by the analyses of the 11 samples, metabolites, which have the highest molecular weight (Data S1), were identified. Different and similar profiles are shown in Figure  1. The samples of Lavandula angustifolia (cv. "White" and cv. "Blue") and Lavandula × intermedia (cv. "Abrial" and cv. "Grosso") have very similar chemical profiles. These samples showed two main peaks at retention times of 3. 23 min and 4. 10 min (blue rectangle). Samples of Lavandula allardi, Lavandula dentata and Lavandula latifolia showed quite similar chemical profiles. They had the particularity of presenting important peaks at retention times of 4. 31 and 6.
Cluster identification The two important peaks for samples of Lavandula angustifolia (cv. "White" and cv. "Blue") and Lavandula × intermedia (cv. "Abrial" and cv. "Grosso") corresponded to coumaric acid hexoside (compound 8, retention time 3. 23 min) and ferulic acid hexoside (compound 14, retention time 4. 10 min). The presence of these molecules was the first selection criterion. These samples constituted cluster 1 (Table  2 ). In this cluster, species Lavandula angustifolia cv. "Blue" and Lavandula × intermedia cv. ''Grosso'' presented very similar profiles. They both presented a high proportion of rosmarinic acid (compound 53, retention time 6. 52 min) (criterion 2), two molecules derived from cinnamic acid (hydroxycinnamic acid glucoside (retention time 4. 31 min, compound 15) and one unknown substance which is not present in the database (retention time 4.
Bold DNA barcoding markers results From the literature, two potential genetic markers (rbcl (RuBisCo large subunit) (B1) and trnH-psbA (B2)) were selected in the BOLD Database (Barcode of Life Data System). All samples were successfully amplified from total DNA and sequenced. Sequence analysis showed the same length between samples for each marker, respectively, rbcl had 690 bp and trnH-psbA had 273 bp. To analyse the result, an alignment was performed to identify the discriminated species. Moreover, a phylogenetic tree was constructed based on the compilation of rbcl and Trnh-psba (Figure  2 ). The UPGMA tree, which was carried out with the rbcl and trnH-psbA markers, showed that 4 species can be fully differentiated, Lavandula pinnata, Lavandula stoechas cv. "Pedunculata," Perovskia atriplicifolia and Lavandula canariensis.
Specific Lavander DNA barcoding marker results To identify more lavender species, a specific marker for the genus Lavandula was created. This marker was located in the ITS (Internal transcribed spacer) locus (Lav marker). This marker was created thanks to GenBank ® database chloroplast sequences of genus Lavandula. All 11 samples were successfully amplified and sequenced with this marker. The Lav marker presented a size of 490 bp. To identify the species differentiated with this marker, a UPGMA phylogenetic tree was created (Figure  3 ). This tree was built using chimeric genetic sequences. It showed that 5 species can be fully differentiated, Lavandula pinnata, Lavandula stoechas cv. "Pedunculata," Perovskia atriplicifolia, Lavandula canariensis and Lavandula latifolia. As in Figure  2 , two new clusters, D and E, could be identified.
Chemical analysis Chemical analysis enables us to identify 6 clusters and give first elements to differentiate several species of the genus Lavandula. Cluster 1 is characterized by the presence of compounds 8 and 14 as an important part in the discrimination of these group species. However, these F I G U R E 2 Phylogenetic tree of plant samples with rbcl and trnH-psbA genetic markers. Phylogenetic tree built with chimeric sequences of rbcl and trnH-psbA genetic markers results using Geneious ® software with UPGMA method. Branch support was based on 1000 bootstrap replicates and is shown at the nodes. The bar represents 0. 004 substitutions per site F I G U R E 3 Phylogenetic tree of plant samples with Lav genetic marker. Phylogenetic tree built with compilation of rbcl and trnH-psbA markers genetic data using Geneious ® software with UPGMA method.
This chemical method allows us to identify molecules of interest in different lavender species. The presence or absence of some of them gives the keys to group the analysed samples in different clusters. In our study, coumaric acid hexoside, ferulic acid hexoside and rosmarinic acid are very interesting compounds and can be used as first elements to differentiate several species of the genus Lavandula and also to give clues for the identification of related species, especially those present in groups 1 and 2. However, phytochemical similarities between several samples and uncertainties related to natural variability (specific growth stages and conditions) did not allow a F I G U R E 4 Phylogenetic tree of plant samples with a chimeric construction of all genetic marker results. Phylogenetic tree built with all genetic markers (BOLD markers and new specific designed marker) results using Geneious ® software with UPGMA method and the bootstrap values from 1000 replicates.
Context. The nearest stars provide a fundamental constraint for our understanding of stellar physics and the Galaxy. The nearby sample serves as an anchor where all objects can be seen and understood with precise data. This work is triggered by the most recent data release of the astrometric space mission Gaia and uses its unprecedented high precision parallax measurements to review the census of objects within 10 pc. Aims. The first aim of this work was to compile all stars and brown dwarfs within 10 pc observable by Gaia and compare it with the Gaia Catalogue of Nearby Stars as a quality assurance test. We complement the list to get a full 10 pc census, including bright stars, brown dwarfs, and exoplanets. Methods. We started our compilation from a query on all objects with a parallax larger than 100 mas using the Set of Identifications, Measurements, and Bibliography for Astronomical Data database (SIMBAD).
We completed the census by adding companions, brown dwarfs with recent parallax measurements not in SIMBAD yet, and vetted exoplanets. The compilation combines astrometry and photometry from the recent Gaia Early Data Release 3 with literature magnitudes, spectral types, and line-of-sight velocities. Results. We give a description of the astrophysical content of the 10 pc sample. We find a multiplicity frequency of around 27%. Among the stars and brown dwarfs, we estimate that around 61% are M stars and more than half of the M stars are within the range from M3. 0 V to M5. 0 V. We give an overview of the brown dwarfs and exoplanets that should be detected in the next Gaia data releases along with future developments. Conclusions. We provide a catalogue of 540 stars, brown dwarfs, and exoplanets in 339 systems, within 10 pc from the Sun.
Catalogue compilation We started our compilation using the Set of Identifications, Measurements, and Bibliography for Astronomical Data (SIM-BAD) database 2   (Wenger et al. 2000). This database provides information on astronomical objects of interest beyond the Solar System that have been studied and reported in scientific publications. We retrieved 378 stars and brown dwarfs with a parallax greater or equal than 100 mas through the SIMBAD table access protocol (TAP) service 3  with the following query, SELECT * FROM basic LEFT JOIN allfluxes on oid=oidref WHERE plx_value > = 100, which returns the information on the object type, its astrometry, its photometry in U, B, V, R, I, J, H, and K s bands, and, when available, its spectral type. To the SIMBAD list we added 21 cool brown dwarfs from recent parallax programmes, but they are not yet included in the database (Sect.
3. 3). Since the query is based on the parallax, SIMBAD sometimes returns only one object while the literature papers refer to separate components (e. g. very close astrometric binaries and spectroscopic binaries), so we added these components as explained in Sect. 3. 1. We removed objects whose binarity has been refuted by Gaia parallaxes, from confusion with activity, or, on the contrary, confirmation by high-contrast imaging studies. We finally completed the list by adding confirmed exoplanets, starting from existing exoplanet databases and reviewing their status to add only confirmed discoveries (Sect. 3. 4). We discarded five objects that initially passed our TAP SIMBAD criterion. Three of them were the brown dwarfs WISE J053516. 80-750024. 9, WISE J035934. 06-540154. 6, and WISE J154214.
The 10 pc sample and Gaia The ability to fully catalogue and characterise the 10 pc sample renders it a fundamental dataset to test the quality of upcoming Gaia releases. Some example quality checks for Gaia using this dataset are as follows: (i) Catalogue completeness: To check the completeness of the overall stellar sample and white dwarf population, one can extrapolate from the local stellar density as was done by GSS21. (ii) Exoplanet detections: While the bulk of the expected large catalogue of exoplanets detected from Gaia astrometry will only appear in the fourth data release, the first sample of exoplanet detections might already be announced in the third data release. It will likely include a subset of those planetary companions within 10 pc with detectable astrometric signatures (see Sect.
Multiple systems Multiple systems are reported in large databases such as the Catalog of Components of Double and Multiple Stars (Dommanget & Nys 2002) or the Washington Double Star catalog 7  (Mason et al. 2001). Multiplicity is also indicated in the SIM-BAD output (OBJ_TYPE=**). For all multiple system candidates we confirmed that the hypothesis of being part of that system was consistent with the most recent parallax determinations. We discarded five companion candidates: BD+42 2320 with β CVn, BD+02 521 with κ 01 Cet, and 2MASS J12141817+0037297 with GJ 1154, based on their Gaia DR2 parallax, while the companions of HD 50281 AB and BD+43 2796 were identified in the Gaia EDR3 with low parallaxes. In addition, three spectroscopic binary candidates (BD+19 5116 A, BD+19 5116 B, and G 13-22) that are known to be active stars were discarded.