dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparison of regional disaggregation EXIOBASE 3rx shows global land embodied in trade as 25. 8% of global land use, compared to 24. 2% in the aggregated database (Table  2 ) (For equivalent results for all countries in EXIOBASE 3rx, see Additional file 1: S13. ) Comparing country-specific trade balances of land for the databases, there is consistency in which countries are net   importers and exporters, but there is a difference of up to 5. 9% in the balance of land embodied in trade between the databases. The top 20 products (global aggregation of results across all countries) ranked according to aggregation error of land embodied in imports are displayed in Table  3. Remembering that the impacts embodied in imports originating in the non-RoW regions are identical in the aggregated and disaggregated database, these results reflect the effect of disaggregation purely of the EXIOBASE 3 RoW regions.
The land embodied in imports associated with "Products of forestry, logging and related services (  02 )" is the single largest product group, with 66,10,000 km 2 or 30. 2% of total global land use embodied in imports. This product group is somewhat susceptible to regional aggregation error, with a summed difference between the aggregated and disaggregated database of 6,60,000 km 2 or 19. 4% of the total aggregation error observed between the models. In contrast, for "Meat animals nec" and "Hotel and restaurant services (  05 ) "the share of land use embodied in exports is only in the range of 1-2%, but the aggregation error of the product relative to the flow (shown by the "error score") is much higher at 64% and 95% of the value of the estimated flow, respectively. This suggests a large degree of uncertainty due to regional aggregation in the aggregated database.
The last column of Table  3  shows that the aggregation can change the value of the flow by a factor of over five ("Copper ores and Concentrates") where the value in the aggregated database is 17% of the corresponding value in EXIOBASE 3rx. The aggregation error for land embodied in imports for regions sorted by regional error score (Table  4 ) shows that the countries with the largest scores, such as Australia and Malta, have a low share of global imports, although the net effect of the aggregation error for the countries is significant. Countries with a low import share out of total consumption of land, such as Russia, Brazil and Australia (Table  2 ) have the largest aggregation errors. In addition, these countries stand out with a high proportion of land originating in EXIOBASE 3 RoW regions.
Challenges and limitations The inclusion of 214 countries in a single database comes with a trade-off in terms of raw data availability and uncertainty. Whilst country-specific land use, production, and trade data are used (for an overview of the regional data availability in the raw data, see Additional file 1: S2), a lot of data estimation is undertaken, especially for the countries not originally in the EXIOBASE dataset. For the 44 countries originally in the EXIOBASE 3 dataset, it would be expected that the additional disaggregation of the rest-of-theworld regions would improve accuracy. However, for the remaining countries, it must be expected that the uncertainty of individual country estimates are high. Especially when disaggregating small (and trade-exposed) countries the expectation of accuracy is low.
It is common in all input-output studies (and all statistical data) to find a declining relationship between accuracy and volume (whether expressed as GDP, output, or key coefficients) (see for example  (Lenzen et al. 2010 , Karstensen et al. 2015 , Wood et al. 2019 )) for one reason because of the laws of error propagation  (Imbeault-Tétreault et al. 2013). Whilst further work could see the replacement of generic data with more country-specific data, it is still likely that the uncertainty levels of individual countries in the disaggregated database will be high, and it is anticipated that the further development of single-country national account consistent procedures are further developed in order to undertake county specific analysis (see, e. g. ,  Edens et al. (2015) ;  Palm et al. (2019) ;  Hambÿe et al.
In terms of data reconciliation issues, most of the challenges in building EXIOBASE 3rx were related to the SUT balancing where there were contradictions between the initial estimates and the macroeconomic data. Several of these issues were resolved by changing options in the balancing routine that increased the accepted level of deviation (which was set to a cap in the balancing) from the initial estimated SUTs. If this did not work, we used initial technical coefficient estimates from EXIOBASE 2. In several of the remaining unbalanced cases (Additional file 1: S5), the issue is negative value added from the macroeconomic data specifically for International Standard Industrial Classification C and E from the UN National Main Aggregates Database. Resolving this issue is a work in progress.
In 2015, Puerto Rico and the Dominican Republic are the countries whose total land footprints are affected the most by this, with an underreported footprint of 0. 86%. For the aggregated database this effect has different distributional impacts as it affects all countries that import from the RoW region that country A is aggregated to. In addition, it affects the domestic part of the RoW region's footprint as there is not a one-to-one relationship between the output of country A and the land use per unit of output (S). RoW America's land footprint is affected heaviest by this with a change of 0. 25%. In the 49-region version of EXIOBASE 3rx, the change is largest in Latvia (0. 08%). Resolving the issue with unbalanced regions in EXIOBASE 3rx is a work in progress. Using the EEBT approach, we do not distinguish between intermediate and final use of traded products.
Further work The results at hand are the first published results using EXIOBASE 3rx. We restrict our scope to the effect of regional aggregation of land use embodied in trade. However, with the limitations related to the EEBT approach and unbalanced countries in mind, there is still unexplored potential in using the database for land use studies in its current form. Firstly, there are multiple land use extensions available, which allows for studying different land types embodied in trade. Secondly, land use embodied in trade can be studied on a sectoral level as the database includes 200 products harmonized across all regions. Thirdly, the database is a time series from 1995 to 2015 which allows for studying the drivers of land use in form of panel regressions or similar methods. This creates opportunities for following up literature findings that suggest some degree of correlation between income and land use  (Weinzettel et al.
2013; Ivanova et al. 2016 ). Panel regression studies using MRIO time series data also enable predictions into the future, which could help overcome the retrospective scope that is identified as a limitation of MRIO studies, which again could increase policy relevance  (Axtell et al. 2001). Currently only land extensions are processed for EXIOBASE 3rx. However, adding other environmental extensions to the database is a work in progress. More immediately, we chose land use as it is a simple and key indicator of agricultural related impacts. The application of biodiversity characterization factors  (Verones et al. 2017 ) and net-primary productivity  (Kastner et al. 2015; Weinzettel et al. 2019 ) are simple extensions to obtain more policy-relevant work. Furthermore, the correlation (Silva  Simas et al.
2019 ) and even the construction of country-specific physical input-output tables  (Bruckner et al. 2019). In terms of methods, there is further work on expanding the cross-entropy model  (Többen and Schröder 2018)  used for reconciling the bilateral trade data with main aggregates of national accounts and estimates of product output, first, to the balancing of the SUTs and, later, to the simultaneous reconciliation of bilateral trade, SUTs and the physical extensions. The main challenges for the practical implementation of such a concept are the computational requirements due to the enormous size of the database (see the method section for a brief overview of the size of EXIOBASE 3rx). However, recent theoretical work on topological transformations  (Rodrigues et al. 2016 ) and maximum entropy models to reconcile data in physical and monetary units simultaneously (Többen 2017) constitute first theoretical steps to solve this issue.
Conclusion With divergence in environmental results between MRIOs hampering the policy relevance of MRIO studies, it is important to both develop more detailed models, and to get a systematic understanding about the underlying sources of these differences. We have developed a regional extension of EXIOBASE 3 called EXIOBASE 3rx and studied the effect of regional aggregation on land use embodied in trade by comparing results to an aggregated version of the same database consisting of 49 regions. Whilst the disaggregated database is experimental in that a lot of structural economic data are estimated, country-specific data on agricultural and resource output, as well as trade are included. We find that the regional aggregation error for land use embodied in imports on a sectoral level is highly concentrated on sectors with high biomass demand, such as forestry, meat from animals, wood products and hotels and restaurant services.
The effect on regions shows that the balance of land embodied in trade differs with up to 6% between the aggregate database and EXIOBASE 3rx, while the net aggregation error of land embodied in imports for some of the 49 EXIOBASE regions differ up to 68% between the databases. The largest absolute aggregation errors for land embodied in imports are found for Asian imports particularly originating in RoW Asia and RoW Africa. Our findings have two important implications regarding the use of MRIOs for land use studies. Firstly, regions in Asia and Africa should be represented in detail, and higher sectoral disaggregation is necessary for a handful of key sectors. Secondly, we suggest that MRIO developers are aware of the potentially significant effects of regional aggregation and build MRIOs that find the right balance between number of regions and sectors for their studies, while at the same time acknowledging the potential uncertainty introduced by assumptions aimed at closing data gaps in raw data.
Product Total land area of flow (km 2 ) Share of global land area (km 2 ), % Aggregation error (km 2 ) Error score (ε) Share of total aggregation error, % Difference between databases (100% is equal to no difference), % 95 95 159 126 92 98 108 96 106 91 17 93 151 98 181 101 94 99 113 147 19. 4 7. 4 6. 1 6. 1 4. 0 3. 7 3. 5 3. 4 3. 1 3. 0 2. 7 2. 3 1. 5 1. 5 1. 5 1. 4 1. 3 1. 2 1. 2 1. 1 0. 10 0. 14 0. 93 0. 64 0. 12 0. 07 0. 15 0. 29 0. 16 0. 10 0. 94 0. 10 0. 95 0. 14 1. 32 0. 03 0. 43 0. 08 0. 17 0. 73 660,000 251,000 209,000 208,000 135,000 127,000 118,000 117,000 104,000 104,000 93,300 78,400 52,500 51,200 50,500 48,800 44,300 40,600 39,600 39,100 30. 2 8. 1 1. 0 1. 5 5. 3 8. 3 3. 5 1. 9 2. 9 4. 7 0. 5 3. 5 0. 3 1. 7 0. 2 6. 9 0. 5 2. 4 1. 1 0. 2 Products of forestry, logging and related services (02) 6,610,000 Oil seeds 1,770,000 Hotel and restaurant services (55) 223,000 Meat animals nec 327,000 Wood and products of wood and cork (except furni-1,150,000 ture); articles of straw and plaiting materials (20) Products of meat cattle 1,810,000 Food products nec 762,000 Chemicals nec 406,000 Vegetables, fruit, nuts 643,000 Wheat 1,020,000 Copper ores and concentrates 98,800 Cereal grains nec 755,000 Other business services (74) 55,100 Crops nec 374,000 Real estate services (70) 38,200 Cattle 1,510,000 Crude petroleum and services related to crude oil 103,000 extraction, excluding surveying Dairy products 529,000 Furniture; other manufactured goods n.
INFORMATION It is essential that information concerning assay design and rationale is provided. Identification of the intended target, including the sequence accession number or official gene symbol, and the location of the amplicon is critical. The specific oligonucleotide sequences are important to include as they are the fundamental components that determine an assays sensitivity and specificity. If these are not available, as with some commercial assays, the amplicon context sequence should be provided  (102). Detailed data on assay sensitivity and specificity should also be included. Without this information, validity cannot be ensured by the scientific community.
Acknowledgments: We are grateful to  Bio-Rad, COMBiNATi, Dropworks Inc. , Fluidigm, JN Medsys, Qiagen, Roche, Stilla Technologies and Thermofisher  for supporting the dMIQE2020 Guidelines. We would like to thank  Ana Fernandez-Gonzalez  and  Gerwyn M Jones  from the  National Measurement Laboratory (NML).
aeronomie. be, last access: 25 October 2020). In combination with other ground-based and satellite data, the resulting near-continuous high-time-resolution 2-year dataset will serve to better constrain VOC emissions in the remote tropical marine atmosphere and to identify missing sources. Part of this dataset has already been used in a source apportionment study of formaldehyde (HCHO)  (Rocco et al. , 2020). The present paper contributes to the disentanglement of the different sources contributing to the (O)VOC composition at RUN by focusing on the role of biomass burning (BB). It is established from ground-based remote-sensing Fourier transform infrared (FTIR) observations that BB impacts the atmosphere over La Réunion. The BB events affecting the region occur most frequently in southern Africa and Madagascar, but impacts from burning in South America and Malaysia have also been identified  (Duflot et al.
, 2010; Vigouroux et al. , 2012). Seasonality of in situ CO concentrations at RUN indicates that BB plumes also impact the atmospheric composition at the surface  (Zhou et al. , 2018). This was confirmed by the hs-PTR-MS dataset generated at RUN for the OCTAVE project (Fig. A1 ). Biomass burning represents the second largest global source of NMVOC emissions  (Yokelson et al. , 2008; Akagi et al. , 2011). Pyrogenic emissions are reasonably well constrained by numerous laboratory studies (e. g. Holzinger et al. , 1999; Christian et al. , 2003; Yokelson et al. , 2008)  and observations of BB plumes in the atmosphere (e. g. Lefer et al. , 1994; Yokelson et al. , 1999 Yokelson et al. , , 2003;; Jost et al. , 2003; de Gouw et al. , 2006; Vigouroux et al. , 2012; Akagi et al. , 2014). Emission factors for numerous compounds have been compiled recently by  Andreae (2019).
In situ air mass characterisation RUN houses routine instruments characterising in situ air constituents in the context of global networks such as GAW (Global Atmospheric Watch), ICOS (Integrated Carbon Observation System) and ACTRIS (European Research Infrastructure for the observation of Aerosol, Clouds and Trace Gases). A summary of routine measurements from the observatory used in this study is shown in Table  1. A detailed description of these and other operational routine instruments at the observatory can be found in  Duflot et al. (2019) ,  Baray et al. (2013)  and  Zhou et al. (2018). In the frame of the OCTAVE project, a hs-PTR-MS instrument (Ionicon Analytik GmbH, Austria) was deployed at RUN from October 2017 to November 2019. This resulted in a near-continuous high-time-resolution 2-year dataset of volatile organic compounds (VOCs).
Ground-based remote sensing The University of Colorado Multi-AXis Differential Optical Absorption Spectroscopy (CU MAX-DOAS) instrument consists of a scanning (horizon-zenith-horizon) telescope coupled to two ultraviolet-visible grating spectrometers  (Coburn et al. , 2011). Scattered-light solar spectra are collected along lines of sight at different elevation angles above the horizon  (Hönninger et al. , 2004 ) and analysed using DOAS least-square fitting  (Platt and Stutz, 2008)  to retrieve trace gas slant column densities (SCDs) by the QDOAS software package  (Danckaert et al. , 2017). For this analysis, NO 2  (Vandaele et al. , 1998)  and O 2 -O 2  (Thalman and Volkamer, 2013)  were retrieved in a fitting window from 425 to 490 nm, using the further fit settings as described in  Kreher et al.
1 and CAMS_GLOB_BIO v1. 1  (Granier et al. , 2019) , instead of the previous MACCity  (Lamarque et al. , 2010)  and the MEGAN_MACC (Sindelarova et al. , 2014) inventories. BB plume injection heights were also introduced in this update. A full description and validation of the update was reported by  Basart et al. (2019). We used modelled mass mixing ratios at the location of RUN calculated on different pressure levels (1000, 950, 925, 900, 850, 800, 700 and 600 mbar levels) every 3 h (00:00, 03:00, 06:00, 09:00, 12:00, 15:00, 18:00, 21:00 UT) from the midnight forecast at 0. 5 • × 0. 5 • resolution (available at: https://apps. ecmwf. int/datasets/data/cams-nrealtime/ levtype=pl/, last access: 9 January 2020). The CO, O 3 and NO 2 mass mixing ratios are transformed to volume mixing ratios and compared to the in situ measurements.
CAMS near-real-time model simulations The modelled mixing ratios at RUN calculated by the CAMS NRT service are compared to data recorded at the observatory for CO, O 3 and NO 2 (Fig. 3 ). The model bias for CO, during the BB intrusions, is lowest on the 800 mbar pressure level (bias of 9. 7 ppbv), which is closest to the mean pressure measured at the observatory during the same period (792. 8 mbar). Note that CAMS reflects the CO mixing ratios well at Maïdo both during and outside (5. 1 ppbv bias) BB episodes. As CO is a chemically stable compound in the atmosphere, the agreement between model and measurements indicates that synoptic-scale transport and mesoscale mixing with the BB plumes at the location of RUN is sufficiently reproduced by the CAMS NRT model. The O 3 model bias is 16 ppbv during the BB episodes with a maximum bias of 39 ppbv (67 % above the calculated value).
FLEXPART forward simulation A comparison between CO obtained from measurements and the calculated CO from transport of the GFAS v1. 2 emission inventory, simulated by FLEXPART, is shown in Fig. 4. Due the misrepresentation of the orographic profile of La Réunion, ground level at the location of the observatory is only 284 m a. s. l. in the model. The real altitude of RUN (2160 m a. s. l. ) is situated near the boundary between layers 1500-2000 and 2000-2500 m a. g. l. in the FLEXPART output. In reality, mesoscale transport, not resolved in FLEX-PART, mixes the different vertical layers, and data recorded at Maïdo correspond to a mixture between different output levels. In what follows, we consider RUN to be located in the layer between 2000 and 2500 m a. g. l. The model overestimates CO mixing ratios at RUN by 37 and 17 ppbv on average during the BB episodes in 2018 and 2019 respectively.
Carboxylic acids Due to the relatively short global average atmospheric lifetime of HCOOH (τ HCOOH = 2-4 d,  Stavrakou et al. , 2012)  and CH 3 COOH (τ CH 3 COOH ≈ 2 d,  Khan et al. , 2018) , EnRs in aged BB plumes should not be compared to emissions ratios from the literature  (Paulot et al. , 2011). However, as wetand dry deposition are dominant sinks for both CH 3 COOH and HCOOH, their effective lifetime during transport in the FT is expected to be much longer (τ HCOOH ≈ 25 d from photochemical oxidation,  Millet et al. , 2015). The much higher HCOOH enhancement ratio estimated from RUN data (20-30 pptv ppbv -1 ) compared to reported emission ratios (2-4 pptv ppbv -1 ) points to significant secondary production during transport to RUN. Similar to secondary production of CH 3 COCH 3 , we identified HCOOH precursor species from the literature and cross-referenced these with pyrogenic EFs  (Andreae, 2019).
Secondary production of HCOOH was also found in BB plumes over Canada  (Lefer et al. , 1994)  but was not observed in previous ground-based FTIR studies at La Réunion  (Vigouroux et al. , 2012). Enhancement ratios of HCOOH calculated from the Tropospheric Emission Spectrometer instrument aboard the NASA's Aura spacecraft over Africa ranged from 26 to 28 pptv ppbv -1  (Chaliyakunnel et al. , 2016) , consistent with our results. This secondary HCOOH production in BB plumes could account for part of the discrepancy in the global HCOOH budget between models and observations  (Chaliyakunnel et al. , 2016). As these EnRs are inferred from data over biomass burning hotspots in Africa, HCOOH is probably formed primarily close to the source and conserved during synoptic-scale transport towards RUN.
Largescale transport of CO originating from pyrogenic emissions, simulated with FLEXPART, supported this by showing larger CO concentrations at higher altitudes. Finally, the horizontal distribution of CO in the SWIO MBL -calculated with FLEXPART -is multiplied with the EnR values inferred from data at the Maïdo observatory. This provided estimates for the impact of BB on air mass composition in the MBL over the SWIO. We compared the calculated estimates with background VOC measurements in the region reported in the literature. Expected excesses for CH 3 CN, CH 3 COCH 3 , C 6 H 6 and CH 3 OH represent an increase in background concentrations by 60 %-150 %, 30 %-75 %, 15 %-150 % and 25 %-> 100 %, respectively. In the future, synchronous VOC measurements at RUN and marine campaigns should be conducted in order to (i) better quantify the ocean-atmosphere interaction in regions with locally enhanced atmospheric concentrations of these species from BB and (ii) identify the different ageing mechanisms during transport in the MBL compared to transport in the FT.
Acknowledgements. We would like to thank  UMS3365  of  OSU-Réunion  for its support in the deployment of the hs-PTR-MS at Maïdo. Financial support. This research has been supported by the  Belgian Federal Science Policy Office  (grant no. BR/175/A2/OCTAVE ), the  Horizon 2020  ( ACTRIS-2 , grant no. 654109 ), and the  National Science Foundation ,  Division of Atmospheric and Geospace Sciences  (grant no. AGS-1620530 ). Review statement. This paper was edited by  Maria Kanakidou  and reviewed by two anonymous referees.
2. Recent computational advances, specifically deep learning, can help overcome the limitations of collecting large-scale data across contexts. However, one of the bottlenecks preventing the application of deep learning for individual identification is the need to collect and identify hundreds to thousands of individually labelled pictures to train convolutional neural networks (CNNs). 3. Here we describe procedures for automating the collection of training data, generating training datasets, and training CNNs to allow identification of individual birds. We apply our procedures to three small bird species, the sociable weaver Philetairus socius, the great tit Parus major and the zebra finch Taeniopygia guttata, representing both wild and captive contexts. 4. We first show how the collection of individually labelled images can be automated, allowing the construction of training datasets consisting of hundreds of images per individual.
CNNs are thus particularly useful when many features for classification are needed. In ecology, deep learning has been successfully and predominantly applied to identifying and counting animal or plant species from pictures. For example,  Norouzzadeh et al. (2018)  used a longterm database of more than 3 million labelled pictures to train a CNN to automatically recognize 48 African animal species. This CNN can replace the need for manual identification in future studies, which is highly time-consuming, thus promoting a more efficient data analysis pipeline. This, and other examples (e. g. Rzanny, Seeland, Wäldchen, & Mäder, 2017; Tabak et al. , 2019) , highlight the potential for deep learning to help to increase sample sizes, and therefore help resolve many limitations in power for biological studies (e.