dataesr/scientific-paragraphs-categorization huggingface dataset:
Then, in early 2016, there is what appears to be an abrupt jump to a large negative difference (GOSAT lower than OCO-2 by -0. 5 ppm) in the NH. From this point forward, XCO OCO-2    although there is a persistent low difference in the NH for the remainder of the record. Figure  17  shows the XCO OCO- 2  2 data for the combined land H-gain and M-gain data, similar to Fig. 16. The main feature here is that the overall variability is larger compared to the ocean H-gain data, which we attribute to biases introduced by variations in both topography and surface albedo. A slightly positive (red) signal is observed during the September to December months in the SH, especially in 2014, 2018, and 2019. Although additional investigation into such signals is warranted, it is beyond the scope of the current work.
Data availability The ACOS GOSAT v9 XCO 2 data are available on the NASA Goddard Earth Science Data and Information Services Center (GES-DISC) in both the perorbit full format (OCO-2 Science Team et al. , 2019b, https://doi. org/10. 5067/OSGTIL9OV0PN) and in the per-day lite format (OCO-2 Science Team et al. , 2019a, https://doi. org/10. 5067/VWSABTO7ZII4). The monthly super-lite files, containing only the most essential variables for each satellite observation, are available at CaltechDATA (https://doi. org/10. 22002/D1. 2178, Eldering, 2021). The OCO-2 v10 L2Lite files containing the bias-corrected and quality-filtered XCO 2 data are also available on the GES-DISC (OCO-2 Science Team et al. , 2020, https://doi. org/10. 5067/E4E140XDMPO2). The TCCON data for individual stations are available on the CaltechDATA site (see citations listed in Table  8 ).
Moisture source diagnostics To study the origin and transport paths of water vapour to Neumayer Station, the Lagrangian particle dispersion model FLEX-PART  (Brioude et al. , 2013)  enhanced by a Lagrangian moisture source diagnostic  (Sodemann et al. , 2008)  is used in this study. Meteorological data needed for the FLEXPART model are taken from the ERA-Interim reanalysis dataset  (Dee et al. , 2011) , provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). Due to computational constraints, we restricted our FLEXPART analyses to the year 2017, only. Air parcels are traced backwards from the final destination (Neumayer Station) for 10 days, similar to the setup described by  Sodemann et al. (2008). The moisture source diagnostic based on the Lagrangian back-trajectories provides values of "moisture uptake" (in mm day -1 ) on a 1 while it decreases during austral summer down to 1.
9 ‰ (for November 2017, see Table  2 ). For evaluation and calibration of the vapour data measured with the Picarro analyser, we compare the measured humidity values measured with humidity values measured at the meteorological station of Neumayer Station  (Schmithüsen et al. , 2019). These measurements are performed in a distance of 50 meters from the main station building at a height of 2 meters. A comparison of both datasets indicates that the Picarro analyser data are reliable ( Fig. 3 ). The correlation coefficient between these two totally independent humidity measurements is 0. 97 (N = 12198, hourly values between 17 February 2017 and 22 January 2019). The relationship between these two series of humidity measurements (q P icarro = 1. 5q meteorology + 0. 08, standard error of the estimate = 0.
Thus the total height difference between the Picarro inlet and the height of the meteorological humidity measurements is approximately 22 m. To test if contamination by exhaust gases could be another reason for this data mismatch, the wind direction was analysed for those hourly Picarro humidity values which are much higher than the corresponding humidity values measured by the meteorological station. Most of the outliers coincide with a wind direction from the south (and a few from the east), which excludes the possibility that a contamination by exhaust gases is the reason for the unusually high Picarro humidity values.
In this wind pattern, relatively higher temperature and humidity leads to more enriched δ 18 O values. During days with extreme low temperatures, the wind typically comes from south to southwest, and winds are generally weak. This weather pattern occurs when a cyclone has moved eastward, so that the former low-pressure area is replaced by a high-pressure ridge. In such a situation, wind speeds decrease  dataset has a bias with respect to the observed frequency of southerly winds. The main reason for this bias in the ERA-Interim data might be the low number of stations in Antarctica, which are used to generate the ERA-interim reanalysis dataset. Due to this bias in the ERA-Interim data, the simulated moisture uptake and vapour transport pathways during cold events, when katabatic winds from the south occur at Neumayer Station, should be taken with caution.
95 ‰ ‰ -1 ) for the period 1981 to 2006  (Fernandoy et al. , 2010). Schlosser et al. (2004)    The performed moisture source diagnostics based on back-trajectory simulations show that the moisture origin for Neumayer Station depends on the season. With the frontal zone moving north in summer, moisture uptake for Neumayer is closer to the coast in summer, whereas in spring and fall the moisture has its origin in a wider region reaching farther north. However, the presented back-trajectory simulations are least reliable for cold periods due to a strong bias in the ERA-Interim data set concerning wind speed and the frequency of southerly winds. This particularly affects our analysis based on back-trajectory simulations when cold, dry and thus depleted air is advected from the continent. Also, the very high variability of surface pressure around Antarctica has to be considered, and a longer study period would be desirable in order to get more reliable results.
Due to the limited period of two years, the dataset is too short to determine if any seasonal cycle of d-excess exists in our measurements. This is even more true, as the uncertainty of the measured isotope values depends on the humidity amount, which is much lower at Neumayer Station in austral winter as compared to the summer season. Based on the determined mean uncertainty of the 1-hour Picarro data (Chapter 2), we estimate the monthly average uncertainty for d-excess in austral winter as high as 4. 5 ‰ (for July 2017) Station.
In the case of the region Ile-de-France (IDF), based on data from surveys conducted between 2012 and 2015, when CH 4 enhancements were measured downwind from sources, narrower signature ranges were found: from -55. 3 ± 0. 1‰ to -51. 9 ± 0. 1‰ for wastewater treatment plants (WWTP) and from -43. 4 ± 01‰ to -33. 8 ± 01‰  14  for natural gas storage facilities. In 2015, according to the Air quality agency of Ile-de-France (AIRPARIF), the total estimated CH 4 emissions from IDF were 30 kt/year. 15 The AIRPARIF emission inventory for the region (including Paris city) uses a bottom-up technique based on sectoral approaches, emission factors, and activity data. The largest CH 4 contributions in IDF were from waste management (42%), and the energy sector (31%). 15 Additionally, the residential and tertiary sector contributed to 13% of the total CH 4 emission in IDF.
12, 19, 20 The uncertainty of isotopic signatures determined with the AirCore sampler depends on the instrument precision, the observed CH 4 enhancement above background (higher enhancements lead to lower δ 13 CH 4 uncertainties), and on the number of data points used for analysis. 12 In the setup used in this study, the replay mode is equivalent to increasing the sampling frequency by a factor of 3. Therefore, we used the AirCore to measure isotopic signatures only for significant CH 4 enhancements above background. On the basis of all our observations, we chose to define a CH 4 enhancement as "significant" if its maximum mixing ratio is greater than 500 ppb above local background. In previous work, where the AirCore was a part of a mobile setup, the same threshold was also used. 12, 20 Local background values were calculated as the mean CH 4 mixing ratio measured immediately before and after each leak indication in the replay mode.
85. Twelve of the 28 AirCores samples fulfilled the criteria, with two AirCores measuring the same leak indication. Details of using the AirCore sampler and isotopic data processing are presented in S4. Estimation of the Leak Indications' Emission Rate. While the source strength of each individual leak indication remains challenging to estimate, especially in urban areas, Weller et al. ,  8  building on von Fischer et al. ,  7  proposed an improved equation, using a statistical calibration model: ln(M CH ) 0. 988 0. 817 ln(CH emission rate) 4 4 = - + × (1) M is the maximum CH 4 enhancement above the background of the leak indication [ppm], and emission rates are estimated in L/min. The method is developed and tested for point sources of methane situated at ground level in an urban environment.
Funding This work was partly funded under European Union's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie  grant agreement No  722479. This work was partly funded under the Climate and  Clean Air Coalition (CCAC) Oil and Gas Methane Science Studies (MSS) , hosted by the  United Nations Environment Programme. Funding was provided by the  Environmental Defense Fund, Oil and Gas Climate Initiative ,  European Commission , and CCAC.
Riboprobes were hybridised for 16 h at 65°C and embryos were incubated with anti-DIG-AP (Roche, dilution 1/4000) overnight at 4°C. Colorimetric detection with BCIP/NBT (Roche) was used. Mounted embryos were imaged on a Nikon Eclipse E800 microscope equipped with a Nikon DXM 1200 camera running under Nikon ACT-1 software. Brightness and contrast were adjusted using FIJI, some of the images used for illustration purpose were created from an image stack, using the extended depth of field function of Photoshop CS5. Area, distance and angle measurements were performed using FIJI  (Schindelin et al. , 2012).
Live imaging was performed approximately from 10. 5-11 hpf to 24 hpf every 2. 5-3 min, using a Phaseview Alpha 3 light sheet apparatus, coupled with an Olympus BX43 microscope and using either a 20X/NA 0. 5 Leica HCX APO objective or a 20X/NA 0. 5 Olympus objective. Images were acquired using QtSPIM software (Phaseview), which controlled a Hamamatsu ORCA-Flash4. 0 Digital sCMOS camera. Room temperature was maintained at 24°C by air conditioning and the chamber temperature was further controlled by a BIOEMERGENCESmade thermostat. Medium level was maintained by a home-made perfusion system and an overflow to renew the medium. The orthogonal illumination of the SPIM induced minimal photo-damage, and embryos developing for more than 20 h under the microscope were alive with a normal head shape at 48-60 hpf, even though the tail was usually twisted due to the mechanical constraint in the low-melting agarose.
Movie analyses Morphogenesis Macroscopic analyses result from quantifications made on n=4 eyes for each morph. Images were obtained and visualised with Arivis Vision4D software using re-oriented 3D stacks to allow similar optical section plane of analysis in different samples, cutting through the middle of the lens and the optic stalk at all time-steps. On one time-step per hour, measurements were performed on the re-oriented images: optic vesicle/optic cup length (at the widest), OV size increase (calculated by subtracting the length at the onset of furrow formation to the length at time t), optic stalk width, distance between the anterior optic cup and the lens, distance between the posterior optic cup and the lens, distance between the optic cup edges, position of the lens relative to anterior OV (=distance between centre of the lens and anterior OV/(distance between centre of the lens and anterior OV+distance between centre of the lens and posterior OV) (see schemes in Fig.
Acknowledgements We thank  Jean-Paul Concordet  and  Anne De Cian  ( Tacgene, Sorbonne Universite, Paris ) for sharing Cas9 protein;  Steṕhane Pere ,  Diane Denis ,  Krystel Saroul ,  Jocelyne Gaget , the Amagen personnel for advices and care of our Astyanax colony;  Patrick Para  for making custom tools for live microscopy;  Adeline Boyreau ,  Adeline Rausch ,  Fanny Husson ,  Elena Kardash  and  Nadine Peyrieras  ( BioEmergence, Gif sur Yvette, France ) for reagents, discussions and advices on live imaging, and for the use of the SPIM and image analysis tools;  Cyprian Wozniak ,  Arthur Le Bris  and  Gaël Launay  from  PhaseView  ( Verriere-le-Buisson, France ) for technical support and development of tools on the light-sheet microscope;  Guillaume Plongeon  for volume analyses;  Martin Pepin  ( Sorbonne Universite ) for writing an essential macro in FIJI and  Romain Le Bars  at the  I2BC Imaging Plateform facility  for assistance on the FIJI software;  Jean Yves Tinevez  and the  Image Analysis Hub at the Institut Pasteur (Paris)  for help with cell tracking strategy.
As one can see on the upper right part of the photo (Figure  1b ) between rs-ZnO nanograins exist intergrain boundaries that could contain water molecules absorbed on the w-ZnO surface before the high-pressure synthesis. As follows from Ref. [4] , this water probably plays the role of "glue" and helps to stabilize metastable rock salt phase at ambient pressure. Elimination of these water molecules will inevitably lead to reverse transformation to wurtzite phase. Heat capacity at constant helium pressure (35-60×10 -2 Pa) and in constant magnetic field (0. 187 Oe) was measured at 40 different temperatures between 310 and 2 K, and two measurements were performed at each temperature. The data were collected using calibrated heater-thermometer platforms in Physical Property Measurement System apparatus by Quantum Design.
Calorimetric measurements were carried out using C80 CS Evolution Calvet calorimeter (SETARAM Instrumentation) equipped with 3D Tian-Calvet sensors. Calisto program package (ver. 1. 086, AKTS AG) was used for running the experiment, data acquisition and initial data processing. Precise temperature calibration over the entire working range (293-540 K) for various modes was performed using the melting points of the standards (gallium, indium, tin); dynamic calibration was carried out by the Joule effect using E. J. 3 unit and a dedicated calibration cell (S60/1434). It was additionally verified by measuring the melting heats of the standards. The second series of calorimetric measurements was performed using a micro DSC-7 evo Calvet calorimeter (SETARAM Instrumentation) equipped with 3D Calvet-type sensors.
Calisto program package (v 1. 086, AKTS AG) was applied for running the experiment, data collection and initial data processing. Calorimeter calibration for heat production was done by the Joule effect using a E. J. 3 calibration unit according to recommendations of SETARAM. Temperature calibration at various heating rates was performed with standard substances according to the IUPAC recommendations. Вatch calorimetric vessels 1. 0 mL made of Hastelloy C276 were used. Stepwise heating mode was used for Cp data collection as recommended for precise thermodynamic measurements  [10]. Two consecutive runs with exactly the same heating modes (one -with the ZnO sample, another -without it -the base line) were performed. The durations of the linear heating step and isothermal step were selected taking into account the time constant of the calorimetric sensor along with the vessel.
Linear heating (5 or 10 K/min) was applied. DTG measurements employed for in situ probing the reverse rs-ZnO-to-w-ZnO transformation were also used for evaluation of the amount of absorbed water. Typically, the H2O loss in the range of 2-4 wt% depending on the sample was observed. These values can be considered as an underestimate, while the heat capacity contribution shows the water amount of ~5 wt% (this value should be taken with precaution since Cp of absorbed water is higher than that of bulk water  [11] ). The correction to heat capacity has been made using the reference data for water in the 2-273 K and 273-310 K temperature ranges  [12] : H 2 O Cp (J mol -1 K -1 ) = 18(7. 7310 -3 T(1-exp(-1. 26310 -3 T 2 ))-7. 5910 -3 +2. 50910 -3 T-1. 47210 -5 T 2 -1. 61710 -9 T 3 +8.
, 2011)  to make it simulate groundwater table levels. Section 2 recounts the process that led to designing this adaptation and the calibration and evaluation schemes of the new model. Section 3 presents the hydroclimatic dataset of 107 catchments over mainland France that was used to evaluate the new calibration with respect to the original one, performed only on streamflow. Section 4 summarises the results and proposes recommendations for model calibration in various contexts. 2 Hydroclimatic dataset.
Data sources Climatic data were taken from the SAFRAN re-analysis  (Vidal et al. , 2009)  by Météo France; they are available at a daily time step for the 1958-2018 period. Daily potential evaporation was computed using the formula by  Oudin et al. (2005). Streamflow data were retrieved from the French national database Banque Hydro  (Leleu et al. , 2014; SCHAPI, 2021). These hydroclimatic data are aggregated at the catchment scale and at a daily time step for mainland France in the HydroSafran database  (Delaigue et al. , 2021) , maintained by INRAE. Groundwater level data are from the French national database ADES (BRGM, 2021), which gathers piezometric data from many providers in the French territory. Selected piezometers were taken from two reference networks, to ensure the quality of data: RNESOUPMOBRGM -national quantitative monitoring network managed by BRGM, the French national geological survey -and RNESP -heritage national network for groundwater monitoring.
Sample selection The French national reference cartography of hydrogeological formations BDLISA  (Brugeron et al. , 2018)  was used to select a sample of catchments and for each catchment, one or several piezometers monitoring relevant aquifers were chosen. First, using the provided metadata, each piezometer extracted from the ADES database was associated with a hydrogeological entity in BDLISA, representing an aquifer. Catchments in which anthropic activities -dams, major direct withdrawals or inflowsare known to have a significant influence on streamflow and catchments in which more than 10% of precipitations fall as snow were discarded. Then, for each catchment, piezometers associated with aquifers emerging inside the catchment boundaries were listed and maps -see an example in figure 2 -were produced to assess the relative importance of each hydrogeological formation for the catchment.
After this spatial selection, the available groundwater level and streamflow data were examined. An initial visual inspection of the time series was performed to eliminate data of too low quality. Then, catchments and piezometers were selected according to the following criteria : -At least 20 years of continuously available streamflow data with less than 10 % of missing data; -At least 20 years of continuously available groundwater level data with less than 10 % of missing data; -At least 10 years of continuous contemporaneity between streamflow and groundwater level. Figure  2  shows two situations encountered at this stage: on the left, the Sensée catchment is connected to one monitored aquifer but three piezometers are available. In this case, the piezometer with the longest time series, with respect to contemporaneity with streamflow data, was selected to represent the aquifer.
Study of the model correlation with piezometry To adapt the existing model structure for groundwater level simulation, we followed a approach similar to other lumped conceptual models, i. e. using a store as a representation of the aquifer, regarding the water content in the store as groundwater level. With this aim in mind, a correlation study was performed on the dataset presented in section 2, in order to identify which of the three conceptual reservoirs of the model structure was the most correlated with piezometry. GR6J was calibrated for the 107 catchments of the dataset using the Nash-Sutcliffe efficiency criterion computed on the square root of streamflow. Then, the time series of model states obtained -the levels of the three conceptual stores and simulated streamflow as control data -and the groundwater level time series were aggregated at a monthly time step, to avoid problems caused by missing piezometry measurements.
In order to find a compromise between these two objectives, 51 values are explored from 0 to 1 by a step of 0. 02. For each value of α, C(α) is maximised as a function of eight parameters. The parameter space transformations described in appendix B are used to convert the optimisation space into the hypercube [-9. 99; 9. 99] 8. The differential evolution global optimisation algorithm -implemented in the RcppDE R package  (Price et al. , 2006; Mullen et al. , 2011; Ardia et al. , 2011a, b; Eddelbuettel, 2018; Slater et al. , 2019; Ardia et al. , 2020; R Core Team, 2021)  -is then executed to find the global optimal point for the eight parameters.
Split-sample test evaluation scheme To assess the effect on streamflow simulation of the new calibration scheme described above, a split-sample test  (Klemeš, 1986 ) is conducted for each catchment/piezometer couple of the assessment dataset described in section 2. For each couple, the available data are divided into two time periods P 1 and P 2 of equal length, defined so as to encompass the same number of data points for which both groundwater level and streamflow are available. Thereby, both periods contain the same amount of information and can be equally used for calibration and validation. Before each period, a warm-up timespan of 5 years is set: the model is run on this period but the resulting simulated values are not used to compute criteria. After determining these periods, the adapted model structure is calibrated on P 1 using C(α), for each value of α; the parameter set obtained is then used to run the model on P 2 and compute several validation criteria.
As for the groundwater level simulation, the performance does not follow the same spatial distribution as streamflow. High  To assess the influence of the geological context, the test dataset of 160 catchment/piezometer couples was divided into six groups, detailed in table  3. The groups were established in accordance with the hydrogeological formation attributed to each piezometer, in the BDLISA reference inventory by  Brugeron et al. (2018). This classification may look arbitrary or inaccurate, For the absolute streamflow simulation performance, the original calibration framework yields high values of NSE for groups 2, 4 and 6, medium ones for group 1 and lower scores for groups 3 and 5. These patterns are found again for the composite calibration framework, even though the distribution of performances for group 5 is narrower.
Acknowledgements. This work is part of the  CIPRHES  project, funded by the  French national research agency ANR  (grant # ANR-20-CE04-0009 ), and a PhD funded by the  French ministry of Environmental Transition (MTE). The stream network shown in maps is from the BD CARTO database (IGN, 2021). We would like to thank  Delphine ALLIER  ( BRGM ) for the help in selecting the test dataset and  Jean-Baptise BOISSONNAT  ( INRAE   Antony ) and  Benoît GÉNOT  ( INRAE   Antony , now at  U. R. B. S. ) for the database maintenance. We extend our warmest thanks to  Charles PERRIN  ( INRAE   Antony ),  Paul ASTAGNEAU  ( INRAE Antony ) and  François BOURGIN  ( INRAE Antony ) for.
Code and data availability. Streamflow data are available on the Banque HYDRO website (SCHAPI, 2021), their use is limited to particular conditions. Climatic data are avaiblable upon request to Météo France for research use. Groundwater level data are available on the ADES website (BRGM, 2021); their use is conditioned to the Etalab open licence. The original version of GR6J is available in the open-source R package airGR  (Coron et al. , 2021).