dataesr/scientific-paragraphs-categorization huggingface dataset:
The difference in AIC (dAIC) between the two models varies depending on the tree structure and characteristics of the alignment. To estimate the expected distribution of dAIC under no coevolution given the rbcS gene tree, we obtained a null distribution of dAIC by simulating sequences of 2,000 bp on the rbcS tree under the independent substitution model. We estimated the support for the Coev model for the ca. 4 million pairs of sites based on this simulated dataset and determined the dAIC value representing the 95% percentile of this distribution (for details, see  Dib et al. , 2014). We then combined the dAIC values with an s/d ratio (ratio between the parameters s, representing the rate of change away from the coevolution profile, and d, representing the rate of change towards the coevolution profile) lower than 0.
S1  at JXB online). Among those, the ones with the strongest signals (dAIC more than 35 and s/d ratio less than 0. 1; Supplementary Fig. S1 ) were selected and the R package qgraph  (Epskamp et al. , 2012)  was used to visualize them. We also plotted the pairs of sites selected on the known protein structure of S. oleracea  (Supplementary Fig. S2; Chain B, C, E and H of 1RCX of Protein Data Bank: Taylor and Andersson, 1997) using PyMol v1. 3 (Schrödinger, 2015).
g. Setaria italica copies 4 and 5). Homology modelling was performed using Modeller v9. 17  (Eswar et al. , 2008). The RuBisCO structure of Oryza sativa (1WDD of Protein Data Bank;  Matsumura et al. , 2012)  was used as a template. The homology modelling was run 100 times for each structural complex of rbcL-rbcS and the best model (the one with the lowest DOPE score) was selected for further analyses. These models were then repaired with FoldX v4. 0  (Schymkowitz et al. , 2005)  using the RepairPDB function. The repair step is mandatory for removing potential bad contacts (i. e. Van der Waals clashes) in the structures, which may cause instability of modelled protein. Also using FoldX v4. 0, we predicted the differences of Gibbs free energy between maximum likelihood model and null model (ΔG) of each estimated structure using the 'Stability' function, with default parameters.
Phylogenetic tree of rbcS among angiosperms We identified two rbcS lineages (rbcS lineages 1 and 2) that could represent a deep duplication event that occurred before the divergence of eudicots and monocots. One gene lineage includes genes that cluster together with a known expressed gene in photosynthetic organs of rice (OsRbcS2;  Morita et al. , 2014) ; we refer to this gene lineage as rbcS lineage 1 (Fig. 1 ). The second gene lineage includes gene copies expressed in non-photosynthetic organs such as OsRbcS1 in rice  (Morita et al. , 2014) ; we refer to this lineage as rbcS lineage 2 (Fig. 2 ). The phylogenetic tree of rbcS lineage 1, including 146 sequences available for 42 species, is shown in Fig. 1  (see Supplementary Table  S1  for correspondence of gene copy name in Fig. 1  and gene ID in the Phytozome v12 database).
We show this table to demonstrate that the estimated number of rbcS gene copies varies between species, but given uncertainty in the quality of genome assemblies, Fig. 1. Maximum likelihood tree of rbcS lineage 1 in angiosperms. The phylogenetic tree was reconstructed in PhyML v3. 0  (Guindon and Gascuel, 2003)  using a TN93 model. Each gene copy of a given species is identified by the species name and distinguished by a number (see Supplementary Table  S1  for correspondence of gene copy name used in this figure and gene ID in Phytozome database). Names of plant families are indicated on the right next to the species names except for Citrus clementina and Ricinus communis. Because the gene copies of these two species were spread all over the tree and did not cluster within a family, their family names are not indicated in this tree.
RBCS RBCL Fig. 4. Coevolving residues between RBCS and RBCL. Coevolution between paired combinations of rbcS sites and rbcL sites was estimated by a maximum-likelihood implementation of Coev and dependent model using nucleotide sequences. The differences in AIC between pairs of models (dAIC) were calculated. AIC of the null model (9. 893) was used as a threshold. We then filtered further with the s/d ratio threshold (0. 1) according to previous studies  (Dib et al. , 2014 (Dib et al. , , 2015)). The 28 coevolving profiles with the strongest signals (dAIC >35 and s/d ratio <0. 1) were selected (see Table  2 ). We identified the corresponding amino acid residues and plotted these sites using the qgraph function of R  (Epskamp et al. , 2012). The residues of RBCS and RBCL are shown by pink and green filled circles, respectively and the numbers in the circles indicate the residues of known RuBIsCO structure (1RCX of Protein Data Bank;  Taylor and Andersson, 1997).
There is clearly the need to study in more detail the exact roles played by gene copies of rbcS lineage 2 in angiosperms,   (Eswar et al. , 2008). The structure was repaired by the RepairPDB function of FoldX v4  (Schymkowitz et al. , 2005). The stability of the whole RuBisCO was estimated using the 'Stability' function of FoldX4. Then, the result of protein stability was taken as a trait and phylogenetic relationships were given as input trees. We then drew a phenogram using the phytools package  (Revell, 2012)  in R (lower panels). Sit5 is shown as representative of Sit4/Sit5 because of synonymous substitutions. in particular, to understand if these copies are functional in all the species carrying this lineage. If this is the case, we will still have to investigate how gene copies of rbcS lineage 2 may contribute to the improvement of catalytic properties of RuBisCO given they do not exist in all the species.
These results suggest substantial interactions between the subunits. However, the coevolution is not occurring between a specific rbcS gene copy and rbcL. Further, the differences of the evolutionary history of each of the gene copies do not lead to differences in the stability of the RuBisCO. We thus propose: (i) that rbcS gene copies are created under neutral evolutionary processes, or (ii) that different copies are kept by the selective pressure that allows plants to cope with different environmental conditions or to be expressed differently in each organ. We need to further investigate the mechanism and the rate of gain and loss of rbcS. Transcriptome data of rbcS in different organs and different conditions (e. g. temperature, aridity) may help us to understand if these copies are playing a role in maintaining stoichiometry.
Acknowledgements We sincerely thank  Dr. Romain A. Studer  for his help in setting up the experiment and with the advice and invaluable help that he provided for the analyses of homology modelling. We would like to thank  Victor Rossier  for his help during data collection and two anonymous reviewers who helped us improve the manuscript. The bioinformatic analyses were performed at the  Vital-IT  facilities of the  Swiss Institute of Bioinformatics. This project was funded by  Swiss National Science Foundation  (grant number  31003A_138282 ) to NS. GAB was supported by the LABEX "TULIP" managed by  Agence Nationale de la Recherche  ( ANR-10-LABX-0041 ) and LABEX " CEBA " ( ANR-10-LABX-25-01 ).
In Section 3, by adapting and improving some cloud-screen algorithms from MAX-DOAS instruments, a new algorithm for high-latitude ZS-DOAS measurements is introduced. This algorithm is applied to UT-GBS and SAOZ TCO retrievals, to help identify the weather conditions during the measurements and to improve measurement accuracy. In Section 4, both the standard and cloud-screened ZS-DOAS TCO data are compared to Brewer direct-sun and modelled TCO data. Random uncertainties are estimated for all ZS-DOAS TCO datasets using a statistical uncertainty estimation model. A discussion of scientific significance and conclusions is given in Section 5. In short, by generating long-term ozone time series that are unbiased by meteorological conditions, this work will help the validation of satellite algorithms for cloudy scenes  (Fioletov et al.
, 2013; Zhao et al. , 2016a). Over the last 18 years, several components of the instrument have been changed. The field-of-view (FOV) of the instrument was changed from 2 to 0. 2 in 2012 after an upgrade of the input optics  (Zhao, 2017). The instrument was upgraded to a ZS/MAX-DOAS system by coupling with a solar-tracker system in 2015  (Franklin, 2015; Zhao, 2017). In 2011 , a reprocessed TCO dataset (1999 -2011)  with the NDACC AMF LUT version 1. 0, was used for satellite validation  (Adams et al. , 2012). In the current work, the latest NDACC AMF LUT (version 2. 0) is used in the TCO retrieval. In this work, UT-GBS measurements made at the PEARL Ridge Lab from 2010 to 2017 are used. For this period, the UT-GBS was operated with a 600 groove per millimeter grating, and recorded spectra between 350 and 560 nm with resolution of 0.
4-2 nm  (Adams, 2012; Zhao, 2017). The UV-visible spectra were processed using the QDOAS software  (Danckaert et al. , 2015)  using daily reference spectra. Due to the decreased resolution at the edge of CCD, the ozone differential slant column densities (dSCDs) were retrieved in the 450-545 nm window, instead of the NDACC recommended 450-550 nm window. Following the NDACC recommendations  (Van Roozendael and Hendrick, 2009) , cross sections of ozone  (Burrows et al. , 1999) , NO 2  (Vandaele et al. , 1998) , H 2 O  (Rothman et al. , 2005) , O 4  (Greenblatt et al. , 1990) , and Ring  (Chance and Spurr, 1997)  were all fitted, and a third-order polynomial was included in the DOAS analysis. The accuracy of GBS TCO data in the high Arctic (2003-2011) is 6. 2 %  (Adams, 2012; Adams et al. , 2012).
69 was located on the roof of the Eureka weather station main building, which is 15 km away from the PEARL Ridge Lab. In this work, Brewer no. 69 direct-sun spectra were analysed using the standard Brewer algorithm  (Kerr et al. , 1981) , with small changes to the analysis parameters due to the high latitude of the measurements  (Adams et al. , 2012). This Brewer TCO dataset is referred to as Brewer. The random uncertainty of Brewer data is typically less than 1%  (Fioletov et al. , 2005) , and for high-quality data (e. g. , SZA < 71) it is less than 0. 6%  (Zhao et al. , 2016b).
Langley plot method Following  Hendrick et al. (2011) , the ozone dSCDs are converted to ozone VCDs (TCO) using the following equation, 𝑉𝐶𝐷(𝑆𝑍𝐴) = 𝑑𝑆𝐶𝐷(𝑆𝑍𝐴)+𝑅𝐶𝐷 𝐴𝑀𝐹(𝑆𝑍𝐴) (3) where the VCD, dSCD, and AMF are all functions of SZA. The reference column density (RCD) is the residual ozone amount in the reference spectrum that is used in the DOAS analysis. The dSCD is directly obtained by DOAS analysis (using the QDOAS software). The AMF is extracted from the NDACC ozone AMF LUT, based on the latitude and elevation of the PEARL Ridge Lab, day of the year, sunrise or sunset conditions, wavelength, SZA, surface albedo, and ozone column (daily TCOs interpolated from daily or weekly Eureka ozonesonde data). The inclusion of ozonesonde data in the AMF calculations improves the results, especially under vortex conditions  (Bassford et al.
Weather impacts on TCO accuracy Without categorizing TCO measurements by weather conditions, the GBS dataset has a -0. 23 ± 0. 24 % mean bias compared with Brewer, where the uncertainty is the standard error of the mean. Similarly, SAOZ has -0. 16 ± 0. 16 % mean bias relative to Brewer. This result is slightly better than  Adams et al. (2012) , who reported the mean relative difference between the GBS (SAOZ) and Brewer TCO measurements at Eureka as -1. 4% (0. 4%) for 2005-2011. These results (at Eureka) are better than the high-latitude agreement reported by  Hendrick et al. (2011) , who found that SAOZ  TCO (1990 TCO ( -2008) )  were systematically lower than Brewer TCO at Sodankyla (67°N, 27°E) by 3-4 %, with the largest discrepancies in the spring and fall. Hendrick et al. (2011)  suggested that this bias was due to the temperature dependence  (Kerr et al.
, 1988; Van Roozendael et al. , 1998 ;   Kerr, 2002; Scarnato et al. , 2009; Zhao et al. , 2016b)  and uncertainty in the ozone cross-section  (Bass and Paur, 1985)  used in Brewer measurements. The agreement between the GBS, SAOZ, and Brewer in  Adams et al. (2012)  (and this study) is notable given the challenges of taking ZS-DOAS measurements at 80°N, particularly in the summer when measurements within the NDACCrecommended SZA range are not available. With help from the Eureka weather record, we can further explore the datasets and quantify the impact of weather, and improve our understanding of these comparison results. In order to quantify the effects of weather on the ZS-DOAS data, coincident measurements were characterized according to the five major weather conditions from the Eureka weather record observations.
Weather impacts without the cloud-screen algorithm applied The effect of weather on the GBS and SAOZ datasets is clear in the comparisons against the Brewer datasets (Figure  5a ). For clear conditions, GBS (SAOZ) has -0. 30 ± 0. 54 % (-0. 79 ± 0. 28 %) mean bias; while for cloudy condition, this bias increases to 1. 22 ± 0. 71 % (0. 31 ± 0. 61 %). Therefore, there is a 1. 5 % (1. 1 %) difference (statistically significant) between GBS (SAOZ) clear-sky measurements and cloudy-sky measurements; this difference is referred to as the clear-cloudy difference in the rest of this work. This demonstrates that the good general agreement (low bias) between GBS (SAOZ) TCO and Brewer TCO reported in Section 4. 1 is due to a combination of a negative bias in clear-sky conditions and a positive bias in cloudy conditions.
To further study the impact of weather on ZS-DOAS TCO, we use a reference TCO dataset (other than Brewer) whose data quality is not affected by the weather. As described in Section 2. 4, MERRA-2 TCO data has been used in previous studies, and agrees well with Brewer data at Eureka. Comparison results are shown in Figure  5b  and d. There are approximately twice as many coincident measurements for MERRA-2 compared with Brewer. Figure  5b  shows that in clear conditions, GBS (SAOZ) has a -2. 92 ± 0. 42 % (-2. 47 ± 0. 24 %) mean bias; while in cloudy conditions, this bias shifted to a positive value, 2. 35 ± 0. 46 % (2. 21 ± 0. 44 %). Therefore, the clear-cloudy difference for GBS (SAOZ) TCO is 5. 3% (4. 7%), and it is statistically significant. This difference is larger than the clear-cloudy difference relative to the Brewer TCO.
Weather impacts with cloud-screen algorithm applied Comparisons between the cloud-screened ZS-DOAS measurements and the reference datasets are also shown in Figure  5. This algorithm successfully filtered more of the measurements made when clouds had been observed at the Eureka Weather Station. For example, Figure  5c  shows that the number of coincident measurements between SAOZ and Brewer decreased from 227 to 214 for clear conditions. For mostly cloudy conditions, this number decreased from 209 to 122. Note that this algorithm is not designed to simply discard all TCO measurements made in cloudy days, but only to remove individual spectra that are cloud contaminated. For example, even for a cloudy day, if clouds cleared up during part of the twilight period, this algorithm may produce ZS-DOAS TCO data (if other criteria also met, described in Section 3.
Similar improvements can be found for other weather types, especially for most cloudy condition (Figure  6e  and Figure  7e ).  What is the effect of cloudy sky conditions on ZS-DOAS TCO data? For the Eureka datasets, there is a statistically significant difference of 1-5 % between ZS-DOAS TCO measurements made under cloudy and clear-sky conditions.  It has been estimated that clouds contribute 3. 3% random uncertainty in the NDACC ZS-DOAS TCO retrieval  (Hendrick et al. , 2011). Thus by removing the cloud term from the error budget, ZS-DOAS TCO datasets should have their random uncertainty lowered by 1. 3%. Can this value be verified by field measurements? After removing cloudy measurements using weather records, the Eureka ZS-DOAS TCO random uncertainties are reduced by 0. 6-1. 3 %. Note that the 3.
3% random uncertainty in  Hendrick et al. (2011)  is an upper limit of the impact of clouds on ozone AMFs, since it is based on sensitivity tests using parameter values for rather large stratus cloud  (Shettle, 1989). Thus, the findings in this work agree with the results in  Hendrick et al. (2011).  TCO measurements in the high Arctic are challenging (e. g. , low sun and large SZA in early spring). What is the general quality of ZS-DOAS TCO measured at Eureka? Using a statistical uncertainty estimation model, TCO datasets from two ZS-DOAS instruments located at Eureka have been evaluated. GBS TCO has a random uncertainty of 3. 9-4. 0 %, while SAOZ TCO has a random uncertainty of 2. 8-3. 2 %. Both instruments have random uncertainties that are lower than the 4. 7 % reported by  Hendrick et al.
22 ± 0. 71 % (0. 31 ± 0. 61 %). However, if Brewer TCO is replaced by MERRA-2 TCO, during clear conditions, GBS (SAOZ) has -2. 92 ± 0. 42 % (-2. 47 ± 0. 24 %) mean bias; while during cloudy conditions, this bias is 2. 35 ± 0. 46 % (2. 21 ± 0. 44 %). In addition, in the high Arctic, Brewer TCO measurements are only available for a relatively short periods (from April to September), and thus the temperature effect (seasonal bias) in the Brewer TCO dataset is smaller compared to datasets collected at mid-and low-latitudes  (Zhao et al. , 2016b). Thus, it is likely the good agreement between ZS-DOAS and Brewer at Eureka is due to a combination of temperature, cloud, and other.
B. Statistical uncertainty estimation Random uncertainties for ZS-DOAS measurements can be determined using a statistical estimation method  (Grubbs, 1948; Fioletov et al. , 2006; Toohey and Strong, 2007; Zhao et al. , 2016b). As an example, we define the two types of measured TCO (denoted as M 1 and M 2 , for Brewer and ZS-DOAS measurements, respectively) as simple linear functions of the true TCO value (X) and instrument random uncertainties (δ 1 and δ 2 ), and assume that there is no multiplicative or additive bias between ZS-DOAS and Brewer, giving 𝑀 1 = 𝑋 + 𝛿 1 𝑀 2 = 𝑋 + 𝛿 2. ( 6 ) If we assume that the instrument random uncertainties are independent of the measured TCO, the variance of M is the sum of the variances of X (around the mean of the dataset) and δ, If the difference between ZS-DOAS and Brewer does not depend on X (no multiplicative bias), and the random uncertainties of the two instruments are not correlated, then the variance of the difference is equal to the sum of the variance of the random uncertainties, 𝜎 𝑀 1 2 = 𝜎 𝑋 2 + 𝜎 𝑀 1 -𝑀 2 2 = 𝜎 𝛿 1 2 + 𝜎 𝛿 2 2.
( 8 ) Since we have the measured TCO and the difference between the ZS-DOAS and Brewer datasets, the variance of the instrument random uncertainties can be solved by 𝜎 𝛿 1 2 = (𝜎 𝑀 1 2 -𝜎 𝑀 2 2 + 𝜎 𝑀 1 -𝑀 2 2 ) 2 ⁄ 𝜎 𝛿 2 2 = (𝜎 𝑀 2 2 -𝜎 𝑀 1 2 + 𝜎 𝑀 1 -𝑀 2 2 ) 2 ⁄. (  9 ) Equation (  6 ) can be used to estimate the standard deviation of instrument random uncertainties ( are high). Thus, the estimates are only as accurate as the least accurate of these parameters. Following the method in  Zhao et al. (2016b) , the variance estimates can be improved by increasing the number of data points or by reducing variances of X by removing some of its natural variability. Thus, the M 1 and M 2 used in the statistical uncertainty estimation are replaced by so-called residual ozone, which is defined as the difference between the semi-daily measured TCO and its weekly mean.
Atmos. Meas. Tech. Discuss. , https://doi. org/10. 5194/amt-2018-261 Manuscript under review for journal Atmos. Meas. Tech. Discussion started: 17 September 2018 c Author(s) 2018. CC BY 4. 0 License. effect. Answering this type of question about consistency between datasets is important for the NDACC UV-visible network to provide globally harmonized ZS-DOAS TCO datasets. In addition to answering the scientific questions above, this work also provides the following contributions to ZS-DOAS measurements and data processing. (1) A cloud-screen algorithm for ZS-DOAS ozone measurements at high-latitude sites has been developed. This algorithm can be modified and applied to low-and mid-latitude ZS-DOAS measurements. (2) Cloud-screened long-term (2010-2017) ZS-DOAS TCO datasets in Eureka have been produced, implementing the latest NDACC UV-visible network ozone retrieval protocol.
These TCO datasets will be used for validation of space-based ozone measurements by the Optical Spectrograph and Infra-Red Imager System (OSIRIS) and the Atmospheric Chemistry Experiment (ACE) in a future paper. NDACC UV-visible working group for providing NDACC UV-visible ozone AMF LUT. We thank Alexei Rozanov from IUP Bremen for providing the SCIATRAN radiative transfer model.
Acknowledgments The UT-GBS data are available from the  Canadian Network for the Detection of Atmospheric Change  (CANDAC, www. candac. ca)and the  Network for the Detection of Atmospheric Composition Change  (NDACC, https://ftp. cpc. ncep. noaa. gov/ndacc/station/eureka). SAOZ data are available from the  Canadian Arctic ACE/OSIRIS Validation Campaign  (eureka. physics. utoronto. ca). Brewer and ozonesonde data are available from the  World Ozone and UV Data Centre  (https://doi. org/10. 14287/10000001 ). Eureka weather station records are available from  Environment and Natural Resources in Canada  (www. canada. ca/en/services/environment. html). Any additional data may be obtained from  Xiaoyi Zhao  (xizhao@atmosp. physics. utoronto. ca). CANDAC/PEARL funding partners are the  Arctic Research Infrastructure Fund, Atlantic Innovation Fund/Nova Scotia Research Innovation Trust ,  Canadian Foundation for Climate and Atmospheric Science, Canada Foundation for Innovation ,  Canadian Space Agency (CSA) ,  Environment and Climate Change Canada (ECCC), Government of Canada International Polar Year, Natural Sciences and Engineering Research Council (NSERC) ,  Ontario Innovation Trust, Ontario Research Fund, Indian and  Northern Affairs Canada , and the  Polar Continental Shelf Program.
The spring 2010-2017 measurements were also supported by the  Canadian Arctic ACE/OSIRIS Validation Campaigns  funded by  CSA ,  ECCC ,  NSERC , and the  Northern Scientific Training Program. MERRA-2 work at the  Jet Propulsion Laboratory, California Institute of Technology , was done under contract with the  National Aeronautics and Space Administration. Xiaoyi Zhao  was supported by the  NSERC   CREATE Training Program in Arctic Atmospheric Science , the  Probing Atmosphere in the High Arctic (PAHA)  project, and the  Visiting Fellowships in Canadian Government Laboratories program. We thank  CANDAC/PEARL/PAHA PI James Drummond ,  ACE Validation Campaign PI Kaley Walker, PEARL Site Manager Pierre Fogal , the CANDAC operators, and the staff at  ECCC 's  Eureka Weather Station  for their contributions to data acquisition, and logistical and onsite support.
Most studies addressing the future of atoll islands focused on ocean-climate drivers of risk, especially sea-level rise, and disregarded the role of local human disturbances. However, the future habitability of these countries will critically depend on the response of inhabited and exploited islands to oceanclimate pressures. Here, using the Maldives as a case study and based on a database including 608 islands (representing 56. 8% and 86. 0% of the country's land area and population, respectively), we assess the influence of human disturbances on island natural response capacity over the last decade. We show that over the last decade, island change was rapid and primarily controlled by anthropogenic drivers. The great majority of inhabited and exploited islands now exhibit an alteredto-annihilated capacity to respond to ocean-climate pressures, which has major implications for future research and adaptation strategies.