dataesr/scientific-paragraphs-categorization huggingface dataset:
Enhancements of ozone amounts have been observed and modelled in biomass burning plumes  (Thomas et al. , 2013) , with production increasing while the plume ages. It can lead to export of ozone as the plumes are transported by the general circulation on a hemispheric scale (e. g. Brocchi et al. , 2017). Satellite observations have greatly improved our understanding of biomass burning fluxes, resulting in the development of many fire emissions databases such as GFAS (Global Fire Assimilation System;  Kaiser et al. , 2012) , GFED (Global Fire Emissions Database;  Giglio et al. , 2013)  or IS4FIRES (Integrated Monitoring and Modelling System for wildland fires;  Sofiev et al. , 2009). However, biomass burning emissions are still linked to uncertainties compared to other sources of trace gases, such as anthropogenic emissions, for the following three reasons.
, 2008)  and more generally to study their interaction with the African monsoon  (Haywood et al. , 2008). As a complement, the In-Service Aircraft for a Global Observing System (IAGOS) infrastructure (http://www. iagos. org, last access: 7 February 2020;  Petzold et al. , 2015)  equips commercial airplanes with air composition sensors, in order to provide regular sampling over several decades. This dataset has been chosen for this study since has offered a large number of UTLS multi-species measurements (including CO and O 3 ) on an almost global scale since 1994 for O 3 and 2001 for CO. A challenge to tackle when using the IAGOS dataset is to be able to discriminate the most probable sources of the sampled air masses. As explained earlier, CO sources are multiple, and transport pathways through the troposphere can be various.
This is why the analysis of large datasets requires other kinds of additional information, like source apportionment, usually obtainable through a Lagrangian backward transport calculation  (Seibert and Frank, 2004). SOFT-IO  (Sauvage et al. , 2017)  is a recently developed tool coupling backward transport calculation and emission inventories to estimate the contribution of recent emissions to CO anomalies identified in the aircraft measurements. Results from this model are available as added value products for the whole IAGOS dataset. As this study focuses primarily on biomass burning and CO, data from SOFT-IO are used for source attribution and identification of biomass burning plumes. Identified fire plumes in the IAGOS database are then used to assess the ability of global numerical simulations performed by the MOCAGE (MOdélisation de Chimie Atmosphérique à Grande Échelle;  Josse et al.
This work is based on both the IA-GOS database and MOCAGE global CTM, presented, respectively, in Sect. 2. 1. 1 and 2. 2. 1. In Sect. 2. 1. 2, the SOFT-IO products and how they are used in this study to automatically identify biomass burning plumes within the IAGOS dataset are presented. The role of injection height during biomass burning events is investigated in Sect. 3 based on the latest plume rise parameters (both plume top altitude and injection height), taken from the biomass burning inventories GFAS. In Sect. 4, MOCAGE results are evaluated in the upper troposphere against IAGOS observations for the reference year. Finally, the impact of biomass burning at a global scale is analysed in Sect. 5 through MOCAGE sensitivity experiments.
Methodology 2. 1 Data description 2. 1. 1 In situ measurements: the IAGOS database IAGOS is a European research infrastructure providing a dense database of in situ air composition measurements, using medium-to long-haul commercial flights. Initially called the Measurement of OZone by Airbus In-service airCraft (MOZAIC) programme  (Marenco et al. , 1998) , it started in 1994 with aircraft equipped for ozone  (Thouret et al. , 1998)  and water vapour measurements  (Helten et al. , 1999). Carbon monoxide measurements were begun later in December 2001  (Nédélec et al. , 2003). These air composition measurements are complemented with meteorological measurements such as wind direction and speed, and air temperature. The IAGOS programme (http://www. iagos. org; Petzold et al. , 2015)  started in 2011 with the same objectives as the MOZAIC programme, and the same measurements are performed automatically aboard the equipped aircraft, during the ascent, descent and at cruise altitude.
The resulting joint IAGOS-MOZAIC database will be called the IAGOS database hereafter. To complement existing features, greenhouse gases (CO 2 and CH 4 ;  Filges et al. , 2018)  and aerosol measurements  (Bundke et al. , 2015)  are to be instrumented as well in the next few years. On average, 7 to 15 aircraft have been instrumented at the same time to perform daily flights and can result in more than 3000 instrumented IAGOS flights per year. Due to the density of the dataset and its time span, it has been used for different kinds of studies: climatology and https://doi. org/10. 5194/acp-20-9393-2020 Atmos. Chem. Phys. , 20, 9393-9417, 2020 trends (e. g. Cohen et al. , 2018; Gaudel et al. , 2018) , chemistry and physics processes (e. g. Ding et al. , 2015; Zahn et al. , 2014) , as well as model evaluation (e.
CO anomalies C A UT are defined as the difference between the measured CO mixing ratio C UT and the background CO mixing ratio, C UTback. C UTback is evaluated as the regional median seasonal value computed for the whole IAGOS database (see  Sauvage et al. ,   2017 for more details). Finally, only the anomalies superior to the seasonal value of the third quartile of CO anomalies, C UTseason (Q3), are considered. This can be summarized as C A UT = C UT -C UTback if C A UT > C A UTseason (Q3). (1) This calculation is only performed when the plane is considered to be below the tropopause, defined here as the 2 PVU (potential vorticity unit) isosurface. In the tropics, where the definition of the potential vorticity diverges, the aircraft is always considered below the tropopause as the highest cruise altitude is under 12 km.
Simulation general setup MOCAGE dynamical forcing fields are taken from meteorological analysis performed by the ARPEGE (Action de Recherche Petite Echelle Grande Echelle) model operated at Météo-France  (Courtier et al. , 1991). They are available at a 3-hourly time step and linearly interpolated at a 1 h time step to be coherent with the dynamical time step of MOCAGE. For anthropogenic emissions, the MACCity inventory was used at a monthly resolution  (Granier et al. , 2011) , complemented by the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) dataset for aircraft emissions  (Lamarque et al. , 2010). Concerning biogenic emissions, the Model of Emissions of Gases and Aerosols from Nature (MEGAN)-MACC inventory is used  (Sindelarova et al. , 2014). MOCAGE is used in its global configuration, with a resolution of 1 • longitude × 1 • latitude (approximately 110 km × 110 km at the Equator and 80 km × 110 km in the midlatitudes).
A second simulation (referred to as INJH hereafter) using this new representation of biomass burning emissions is performed, identical in every other aspect to the BASE simulation. This new representation of fire injection will be compared to the one in the BASE simulation, using observations of biomass burning plumes from the IAGOS dataset, in Sect. 3. A third simulation (named NOBB) is performed where emissions from biomass burning are simply turned off (not just for CO but all concerned trace gases). It will be used to study biomass burning impact on CO budget in the UT, presented in Sect. 5. A summary of the three simulations and their specificities can be found in Table  2. 3 Improvement of biomass burning injection height in MOCAGE.
Global evaluation of the plume representation For the 220 plumes detected in the IAGOS database over 2013, CO mixing ratios measurements inside the plumes are compared with the MOCAGE model with the following methodology. Hourly results from the global model are interpolated on each plane trajectory at the measurement time and location to perform a collocated comparison. The interpolation is bi-cubic in space and linear over time. This results in a consistent dataset between the IAGOS CO measurements and each MOCAGE experiment, where each data value shares the same space and time coordinates. To perform the IAGOS-MOCAGE comparison, the probability density function (PDF) for each dataset is computed, as well as box-and-whisker plots. The results are first investigated for the plumes originating from the African continent and boreal forests, as they are the most sampled by IAGOS aircraft in 2013 and the main contributors to biomass burning emissions.
Looking at plumes originating from the African continent (either from NHAF or SHAF; see Sect. 2), both the PDF and the box plots are similar between IAGOS and MOCAGE for the two simulations. Both MOCAGE simulations well reproduce the CO mixing ratios distribution when compared to the IAGOS measurements, with a slight negative bias of -11 ppb between the means. The 95th quantile in partichttps://doi. org/10. 5194/acp-20-9393-2020 Atmos. Chem. Phys. , 20, 9393-9417, 2020  ular is 26 ppb lower in both MOCAGE simulations compared to the IAGOS measurements. Comparing both simulations against IAGOS observations, it appears that using plume rise parameters does not improve the representation of the biomass burning plumes, as no significant difference can be seen between the two datasets. Indeed, this could be expected, because pyroconvection rarely drives fire emissions above 3-4 km into the troposphere for African fires.
Lastly, considering the dataset regardless of the origin of the plume (also not shown), African emissions contribute to more than half of the data points; hence, conclusions are the same as for Fig. 6. In order to complement the analysis, statistical scores have also been computed for each dataset of plumes, for the two different MOCAGE simulations against the IAGOS biomass burning plume measurements. The chosen metrics are Spearman's correlation coefficient, the modified normalized mean bias (MNMB) and the fractional gross error (FGE). Those three metrics have been chosen to better represent an atmospheric chemistry model performance against in situ data and have been used in recent studies in a similar way  (Elguindi et al. , 2010; Wagner et al. , 2015). Each metric's calculation is detailed in the Appendix A.
MOCAGE evaluation against IAGOS measurements Before assessing the impact of biomass burning on upper tropospheric CO, the INJH MOCAGE simulation is compared to the IAGOS database for the year 2013. The objective is to validate the ability of MOCAGE to represent realistic CO in UTLS altitude range. For this purpose, we calculate statistical scores for the IAGOS measurements made during the cruise part of the flights to avoid sampling the free troposphere and the PBL happening during ascent or descent of the plane. Statistical scores used are the MNMB and FGE described in Appendix A for the same reasons given in Sect. 3. 1. The results are presented in Table  4 , where scores have been detailed for the three geographical regions defined in Fig. 1. Metrics are also being presented separately for the UT and the LS, with the UT being considered between +15 and +75 hPa below the dynamical tropopause and the LS between -15 and -150 hPa above it.
The MOCAGE results are then averaged https://doi. org/10. 5194/acp-20-9393-2020 Atmos. Chem. Phys. , 20, 9393-9417, 2020 in this layer. The estimation of the contribution of biomass burning is calculated by subtracting the results of the NOBB simulation from the INJH one. The MOCAGE annual mean CO mixing ratios for 2013 in the upper troposphere for the INJH and the NOBB experiments are displayed in Fig. 13 , as well as the calculated difference between the two. A similar map as the INJH but with IAGOS data could not be produced as a year of IAGOS flight is not enough to establish a representative climatology. However, the map looks consistent with the 15-year IAGOS CO climatology shown in  Cohen et al. (2018). It appears that biomass burning emissions contribute globally to upper tropospheric CO.
To validate the results of this simulation, IAGOS in situ observations were used, in addition with SOFT-IO ancillary data on CO anomalies source attribution. Thanks to the SOFT-IO contribution estimations, 220 plumes were selected from the IAGOS flights of 2013, so as to validate the use of GFAS injection height and plume top altitude in MOCAGE. Results were analysed accordingly to the region of origin of the plume. The original plume representation in MOCAGE performs fairly well. Nevertheless, the new injection scheme greatly improves the modelled plumes from boreal wildfires. It was also shown the importance of capturing the variability of pyroconvection on CO vertical distribution at northern high latitudes, but not elsewhere, despite higher occurrences of wildfires. To go a step further, it would be interesting to investigate the sensitivity to the shape of the injection profile of the vertical distribution of CO.
Based on the results of this study and previous ones that have shown that the importance of the transport through pyroconvection has been underestimated  (Fromm et al. , 2019) , it appears that the use of products such as GFAS injection height can offer improvement in the representation of biomass burning plumes in atmospheric models, especially for midlatitudes to high latitudes. Secondly, the INJH simulation was evaluated using all the available CO IAGOS measurements of 2013 before being used to study the contribution of biomass burning to upper tropospheric CO. The extensive IAGOS database has been used in two ways: -Vertical profiles at different airport clusters were used to validate CO levels as well as its gradient in the free and upper troposphere at specific locations. -Overall statistical scores and vertical profiles calculation in the UT and LS confirmed MOCAGE is well suited to study upper tropospheric CO.
As emissions from biomass burning this year are on the lower end, estimations made in this paper are expected to be as well. It would be interesting carry out similar analysis for years with high fire activity or more generally investigate the role of interannual variability of fire emissions by extending the simulations. The IAGOS database would still be valuable as it spans more than 15 years of CO measurements. Before this, the first step will be to extend the analysis of the numerical simulations used in this paper, looking at wildfire impacts on upper tropospheric chemistry. The ozone production especially will be investigated since CO is its main precursor at this altitude.
Acknowledgements. The authors acknowledge the strong support of the  European Commission, Airbus  and the airlines (Lufthansa,  Air France ,  Austrian ,  Air Namibia ,  Cathay Pacific , Iberia and China Airlines so far) who carry the MOZAIC or IAGOS equipment and have been performing the maintenance since 1994. In its last 10 years of operation, MOZAIC has been funded by  INSU-CNRS (France) ,  Météo-France ,  Université Paul Sabatier (Toulouse, France)  and  Research Center Jülich (FZJ, Jülich, Germany ). IAGOS has been additionally funded by the  EU projects IAGOS-DS and IAGOS-ERI. We also wish to acknowledge our colleagues from the  IAGOS team in FZJ, Jülich , for useful discussions. The MOZAIC-IAGOS database is supported by  AERIS. Financial support. This research has been supported by the  Midi-Pyrénées region and Météo-France.
Data availability. The IAGOS time series (https://doi. org/10. 25326/06; Bundke et al. , 2018a), vertical profiles (https://doi. org/10. 25326/07; Bundke et al. , 2018b)  and the SOFT-IO ancillary data (https://doi. org/10. 25326/3; Sauvage et al. , 2018)  used in this study can all be found on the IAGOS data portal (https://doi. org/10. 25326/20; Boulanger et al. , 2019). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-20-9393-2020-supplement.
First we ran the composite likelihood approach implemented in the software stairwayplot  (Liu & Fu, 2015). The stairwayplot is a nonparametric model where N e is free to vary at each coalescent interval. The composite likelihood is evaluated as the difference between the observed SFS and its expectation under a specific demographic history. Foll, 2013). We used the SFS and the mean pairwise differences (π) as summary statistics. We retained the best 5,000 simulations to perform a local linear regression  (Beaumont, Zhang, & Balding, 2002)  and reconstructed the abc skyline at user-specified time points as in Maisano  Delser et al. (2016). Third, we performed a hierarchical approximate Bayesian computation (hABC) analysis with the goal of detecting potential concerted demographic histories across the species under study  (Chan et al.
| Reconstruction of potential habitats given present and past sea level conditions As Rapa Nui and Motu Motiro Hiva are part of the Easter chain composed of numerous seamounts, we examined how eustatic changes modified the surface of potential habitat for Rapa Nui endemic species. We investigated whether periods corresponding to minimum sea level constituted an extension or a reduction of potential available habitat for Rapa Nui endemic fishes using the package marmap  (Pante & Simon-Bouhet, 2013)  in R. We reconstructed both present surface habitat using the maximum depth recorded for all species (160 m) and surface habitat given the scenario of a minimum sea level (sea level lowstand, -120 m context), corresponding to the surface between -120 and -280 m depth.
We thank Luiz A. Rocha for kindly sharing photographs and information related to maximum depth of several Rapa Nui endemic species. We are grateful to the Genotoul bioinformatics platform Toulouse Midi-Pyrenees (Bioinfo Genotoul; http://bioin fo. genot oul. fr/) for providing computing resources. We are obliged to three anonymous reviewers for their constructive and helpful advices. This work was supported by the FONDECYT Postdoctorado fellowship no: 3160692 to E. D. -T. and by the ATM grant 2016 from the Muséum National d'Histoire Naturelle to S. M. E. C. G. thanks the scholarship BECA CONICYT-PCHA/Doctorado Nacional Chile/2019 Folio No. 21190878. P. S. -A. was supported by FONDECYT grant no: 1190710. Finally, we thank S. Hirase and the three anonymous reviewers for providing constructive reviews of earlier versions of the manuscript.
were followed. Specimens were collected under permit No. 724, 8March 2016, obtained from the Chilean Subsecretary of Fishing. The Universidad Austral de Chile Ethical Care Committee and Biosecurity Protocol approved our use and handling of animals. DATA AVA I L A B I L I T Y S TAT E M E N TAll fastq sequence files are available from GenBank at the National Center for Biotechnology Information short-read archive database (BioProject accession no. : PRJNA622670).
Economic evaluation Thanks to the precise results of the process simulation, the model includes the economic evaluation of the remaining process alternatives for the biorefineries. In this study, due to new creation or production bioproduct, we fixed some economic parameters in order to decrease the financial risk. This study is made with the software Aspen Process Economic Analyser, and then the model encompasses the taxes and the depreciation. For the economic study, we fix the price of acetone to 1. 1 $/kg, the price of ethanol to 0. 55 $/kg and the butanol to 4 $/kg (prices fixed in case the oil barrel cost is 100$). For all the processes of the superstructure, the calculations are made with the following hypothesis: -The workshop life is 20 years. -The costs of raw materials are constant.
Environmental evaluation The objective of the ecological study is to evaluate the influence of the biorefinery on the environment. This assessment is significant in the current context of industrial ecology because chemical companies are looking for "green" plants and because of the restrictive constraints on the environment. Many impact factors exist, but our model mainly focuses on Impact 2002+ and IPCC GWP 100a. The Impact 2002+ method contains the evaluation of the human health, the ecosystem quality, the climate change and of the resources. The IPCC GWP 100a method calculates the carbon equivalent coefficient of the biorefinery. The calculations are made on the software SimaPro. SimaPro uses specific databases for the Life Cycle Assessment method. The evaluation is only based on raw materials, utilities, energy consumption, products, wastes and emissions.
Stimulus material and design. Eighty full-color photographs of animated and unanimated objects, plus six as practice, and 80 full-color photographs of indoor and outdoor scene's contexts, plus six as practice, were used. Objects were selected from Konkle et al. 's database  50  or Google Images, contexts were selected from the SUN database  51  or Google Images. "Object" images were defined by a unique object (e. g. , cow, parasol). "Context" images were mainly defined by global, spatial information (e. g. , mountain, beach), even though they also contained local information. Eight versions of each experimental scene used in Experiment 1 were created using Adobe Photoshop CS 9. 0 (Adobe, San Jose, CA). First, a semantically consistent object was inserted within a context (e. g. , the cow in the mountain, the parasol in the beach), starting between 2° and 3° horizontally from the context's closest edge (the small variations of object placement were required to avoid spatial inconsistency within the context).
We repeated the first and second step inserting a mirror reversed object, to control for any effect of viewpoint typicality or eccentricity of its most diagnostic part (e. g. , the handle in a cup). We then mirror reversed each scene to present it in each hemifield (left or right) with the object maintaining the same eccentricity from the central fixation point. For Experiments 2-5, the contexts and the objects were presented in separate images but paired and mirrored in the same way as in Experiment 1. Moreover, each object was pasted into a different colored 1/f noise background, created in Matlab R2015a (MathWorks, Inc. , Natick, MA), which did not evoke any semantics. The objects had the same size as in Experiment 1. They were placed starting at 2° horizontally from the background's closest edge and centered vertically in the background.
We also created, in Matlab, a white noise mask. All experimental images and the mask were shown at 10° × 8° (315 × 252 pixels). Moreover, we created visual response cues. In Experiment 1, the cue was a small white rectangle (3. 24° × 2. 76°, 102 × 87 pixels) with a black outline, where a black rectangular region indicated the scene's quadrant containing the object (this was done to prevent the participant to respond to another portion of the scene by mistake, thinking that it corresponded to the target object). In Experiments 2-5, the cue was a black arrow (2. 5° × 1°, 79 × 32 pixels) pointing toward the screen's region where the image to categorize first had been presented.
Then intrusion responses (errors semantically consistent with the other, co-occurring image when this was correctly categorized, see  10  ), were removed from the analyses (see Table  1  for a summary of data removals in each experiment). All analyses (Generalize Linear Mixed Models, independent-samples t tests) were conducted on accuracy (binary response: correct or incorrect). GLMMs were run using the lmer() function of the lme4 package  53  in the R programming environment (The R Foundation for Statistical Computing, Version 3. 3. 2); see Results for information about the models' structure. For each model, we reported the predictors' coefficients (β-values), SE-values, z-values, and associated p-values. Graphics were created using the ggplot2 package  54. We used GLMMs because they have many advantages over traditional ANOVA models.