dataesr/scientific-paragraphs-categorization huggingface dataset:
Short-term target quality control: continuous measurement repeatability field equivalent In our basic measurement sequence, the air from a highpressure cylinder (STT) is analysed twice a day with a 10 h frequency for at least 20 min to assess the daily performance of the spectrometers. This metric mainly describes the intrinsic performance of the spectrometers and not of the sampling system. It is a field estimation of the CMR and is computed as the standard deviations of the raw data over 1 min intervals, the first 10 min of each target gas injection being filtered out as stabilisation. Figure  6  shows the monthly mean CMR for the combined time series of CO 2 and CH 4 using the same type of analysers. The time series of CMR for CO are shown in the Supplement (Fig. S2 ). For CO 2 , we observe a decrease in the CMR over the measurement periods, indicating an improvement in instrument precision.
The CO CMR results reflect such large differences (shown in Fig. S2  of the Supplement), with the CO CMRs from Los Gatos Research instruments being lower than the CO CMRs from Picarro. The Picarro 187 and 728 CO LTRs are significantly lower than their CO CMRs. This means that their raw data have large high-frequency variabilities but when averaged over several minutes these instruments are quite stable (they are not very sensitive to atmospheric or pressure changes). Overall the precisions measured at the station for CO 2 , CH 4 and CO remain similar to the initial values estimated by the manufacturer and the ATC laboratory, showing no degradation due to the design of the station or the measurement procedures.
Field long-term repeatability The field LTR is computed as the standard deviation of the averaged STT measurement intervals over 3 d as performed during the initial test at the ICOS metrology lab. Data are then averaged every month. The same STT data are used but with a different perspective, more closely linked to the ambient air data uncertainty. Figure  7  shows the monthly mean field LTR of the merged time series using the different instruments and sampling systems. This figure shows the uncertainties of the data related to the analysers (not the sampling systems). As for CMR, CO 2 and CH 4 LTR show decreasing trends suggesting an improvement of the internal performance of the spectrometers built by Picarro and of the air distribution system and data selection/flagging. The early part of 2018 experienced a markedly worse LTR compared to following months.
Station audit by travelling instruments A metric such as CMR is very useful for monitoring the internal performance of instruments and for identifying any  instrument failure as early as possible. Other instrumentrelated metrics such as long-term calibration drift or calibration stability over the sequences are also useful for monitoring instrument performance. However, they do not give an assessment of the overall measurement systems. Flask versus in situ comparisons or station audit by travelling instruments are recognised as essential tools in the performance and compatibility assessment of a measurement system. ICOS audits are performed by a mobile lab, hosted by the Finnish Meteorological Institute in Helsinki, and equipped with stateof-the-art GHG analysers and travelling cylinders. The measurement data from the station are centrally processed at the ATC.
However, the data produced by the mobile lab are computed separately to maintain the independent nature of the Mobile Lab and at the same time to evaluate the performance of the centralised data processing. The OPE station was audited twice, once in summer 2011, soon after the station was set up, during the feasibility study for the travelling instrument methodology, and then in summer 2014, when the ICOS mobile lab was ready for operation. During the 2-week intercomparison in 2011, significant differences for CO 2 and CH 4 were noticed between the Fourier transform infrared (FTIR) travelling instrument and the CRDS reference instrument  (Hammer et al. , 2013). As the two instruments have different temporal resolutions and different response times, the CRDS measurements were convoluted with an exponential smoothing kernel representing a 3 min turnover time to match the FTIR specifications.
03 ppm and 1. 80 ppb for CO 2 and CH 4 respectively. The CO comparison was carried out for OPE LGR and OPE G2401 instruments and compared to the TI G2401: the average deviations exceeded the WMO GAW component compatibility goal (±2 ppb). Vardag et al. (2014)  presented similar intercomparison results at MHD over 2 months in spring 2013. For CO 2 , the difference between the TI and the station analyser (Picarro G1301) for ambient air measurements at MHD was 0. 14 ± 0. 04 ppm. During this intercomparison there were no calibration issues as the same set of calibration cylinders was used on both systems. However there could also have been a bias in the water correction effect. Still, most of the differences between station data and the TI during ambient air measurements remained unexplained. These results and the previously published results highlight the major difficulties that station PIs are facing with intercomparison interpretation and understanding.
Diurnal cycles and vertical gradients The diurnal cycles of trace gases result from atmospheric dynamics (especially the daily amplitude of the boundary layer height), surface fluxes and atmospheric chemistry. The mean diurnal cycles of CO 2 , CH 4 and CO are shown in Fig. 10  for the three sampling levels (10, 50 and 120 m). Despiked hourly data (not detrended or deseasonalised) were used to compute the mean diurnal cycles. CO 2 , CH 4 and CO mole fractions display similar diurnal cycles due to the similar atmospheric dynamics control: a large increase in mean mole fractions and vertical gradient during night-time in contrast to a reduction in the mean of mole fractions and vertical gradients during daytime. During the afternoon, while the CH 4 and CO mole fractions at the lowest level stay larger than those at the top level, the CO 2 mole fractions at the lowest level are slightly lower than those at the higher level.
Regional-scale signal extraction The station time series exhibit strong variability from hourly to interannual timescales. These variations may be related to meteorological variability and to variations in sources and sinks. We are mostly interested in the regional signatures at scales that can be approached using model inversions and assimilation tools. For this reason, we want to isolate the situa-  tions where the local influence is dominant and shadows the regional signature from the time series and data aggregation. We then need to define the background signal to which the regional-scale signal is added. Such local situations and background definitions may be extracted purely from time series analysis procedures, or may be constrained on a physical basis. El  Yazidi et al. (2018)  assessed the efficiency and robustness of three statistical spike detection methods for CO 2 and CH 4 and concluded that the two automatic methods, namely standard deviations (SDs) and robust extraction of baseline signal (REBS), could be used after a proper specification of parameters.
The OPE mean CH 4 annual growth rate over the 2011-2018 period is 8. 8 ppb yr -1 using CCGCRV and 8. 9 ppb yr -1 using the Theil-Sen method. It is slightly larger than the annual increase in globally averaged atmospheric methane from NOAA, which is 7. 5 ppb yr -1 over the 2011-2017 period. A slightly decreasing non-significant trend is seen for CO at OPE over the 2011-2018 period. This finding is consistent  Table  7. Growth rates of CO 2 , CH 4 and CO mole fractions at OPE 120 m level for the period 2011-2018 computed on the afternoon mean data using the CCGCRV and Theil-Sen methods. The 95 % confidence intervals are displayed for each compound and method. OPE-120m CO 2 (ppm yr -1 ) CH 4 (ppb yr -1 ) CO (ppb yr with recent observations in Europe and in the USA  (Lowry et al. , 2016; Novelli et al.
Conclusion The OPE station is a new atmospheric station that was set up in 2011 as part of the ICOS Demonstration Experiment. It is a continental station sampling regionally representative air masses. In addition to greenhouse gases and meteorological parameters mandatory for ICOS, the station measures aerosol properties and radioactivity and is part of the regional air quality network. The GHG measurements are performed in compliance with the ICOS atmospheric station specifications, and the station was labelled by ICOS in 2017. We have presented the GHG measurement system as well as the quality control performed. Next, analysis of the diurnal cycles, seasonal cycles and trends were given for the GHG data over the 2011-2018 period. Finally, we analysed the compound residuals with regard to the air mass history.
The station set-up and its standard operating procedures are also fully compliant with the ICOS specifications  (Laurent et al. , 2017). The diurnal cycles of the three compounds show amplification of the vertical gradient at night mainly caused by the night-time boundary layer stratification associated with ground cooling and radiative loss. Minimum values are reached during the afternoon when vertical mixing is more efficient. In addition to this influence of the main atmospheric dynamics, diurnal cycles of surface emissions and of photochemical processes also play some role in the diurnal profiles of the three compounds. We focused on the afternoon data as we are interested in larger-scale processes. We computed the mean seasonal cycles of CO 2 , CH 4 and CO. Relatively strong positive trends were observed for CO 2 and CH 4 with a mean annual growth rate of 2.
Acknowledgements. The authors gratefully acknowledge the  NOAA Air Resources Laboratory (ARL)  for the provision of the HYSPLIT transport and dispersion model and READY website (http://www. ready. noaa. gov,last access: 8 November 2019) used in this publication. Samuel Hammer  from  Heidelberg University  and  Hermanni Aaltonen  from  FMI  are thanked for their efforts during the OPE station audits. Staff from  IRFU-CEA  are acknowledged for their contribution to the station's initial design and installation. We also thank staff from  SNO-ICOS-France  and from the  ICOS Atmospheric Thematic Center  for their technical support. The authors would like to thank the editor and two anonymous referees, who provided valuable suggestions and constructive comments to improve the paper. Review statement.
Data availability. Data are available upon request to the corresponding author. Data are also available through the ICOS Carbon portal (https://doi. org/10. 18160/CE2R-CC91, ICOS RI, 2019). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/amt-12-6361-2019-supplement. Author contributions. SC, JH, LL, OL and MD performed the instrumental set-up and maintenance. SC, JH and OL carried out the data curation. MR supervised the station operations and provided suggestions for the data analysis, interpretation and discussion. JH prepared most of the figures. SC wrote the paper with contributions from all co-authors.
Thermomechanical properties Firstly, the coefficient of thermal expansion (CTE) is measured. As the sample is polycrystalline, anisotropy was not taken into account and only the average CTE is determined. Smoothed measurements are reported in Fig. 8  for temperature ranging from 20 °C to 800 °C. A trend line could be obtained through equation (  2 ): α(°C -1 ) = 7. 0 × 10 -9 T(°C) + 8. 1 × 10 -6 (2) Thus, the mean CTE value at 20 °C is 8. 2 × 10 -6 °C-1. This value is smaller than the CTE of most of the other crystals used in mid IR OPO systems (cf. Table  1 ). Unfortunately, at this step we do not have any information about data dispersion between values along the different crystallographic axis. Secondly, the thermal conductivity value was measured on the ZGSe polycrystals. This is an important parameter in order to estimate the crystal's potential in term of thermal shock resistance.
Although this method was criticized for criteria such as the age and alcohol consumption, recent consensus meeting of experts has recognized their value and recommended their incorporation into any method. While early studies searching for DILI in large databases especially in electronic medical records were based on codes of diseases or natural language without causality assessment, the recommendation is now to include RUCAM in the search for DILI/HILI. There are still studies on DILI detection or the identification of biomarkers that take into consideration the cases assessed as "possible," although it is well known that these cases reduce the strength of the association between the cases and the offending compound or the new biomarker to be validated. Attempts to build electronic RUCAM or automatized application of this method were successful despite some weaknesses to be corrected.
LITERATURE SEARCH AND DATA REVIEW Published reports were systematically searched in electronic databases of Medline (source PubMed) from 2014 using the search terms: RUCAM, Roussel Causality Assessment Method, DILI, drug-induced liver injury, pharmacovigilance, and CIOMS. From each searched segment, the publications of the first 30 hits were analyzed and selected for reports in English language. Before the final analysis, the publications were assessed regarding the clinical quality and data completeness. The search was completed on 4 April 2019.
Clinical trials should not be excluded from the RUCAM use as it was wrongly suggested  (Regev et al. , 2014). There is no argument to reject RUCAM in clinical trials but just the opposite. Indeed, in this context, it is usual to collect specific and relevant data, and normally, the suspected DILI cases are reported on ongoing basis provided that the flow chart for the management of ALT elevation is available for the investigators and the monitors. It is argued that RUCAM is not adapted to clinical trials although the criteria for causality assessment do not differ qualitatively and quantitatively from the postmarketing setting. The data collected for each case are best used with RUCAM to achieve a correct diagnosis of DILI as shown in a study using a RUCAMbased automated method applied to a dataset from clinical trials  (Scalfaro et al.
In addition, the authors rightly suggested to include automated RUCAM into EMRs for the detection of DILI in hospital-based population. To facilitate case evaluation of suspected DILI in routine pharmacovigilance, an algorithm was proposed based on RUCAM with many supportive tables and named Pharmacovigilance-Roussel Uclaf Causality Assessment Method (PV-RUCAM)  (Scalfaro et al. , 2017). This project was prompted because, in spontaneous reports, data are usually incomplete and PV professionals are not necessarily experts in DILI. The performance of this method was compared in different settings with regard to its applicability and differentiation capacity. The score was applied in two datasets of individual case safety reports (ICSRs) extracted randomly from clinical trial reports and a third dataset of electronic health records from a global PV database.
Computerization of causality assessment method was also recently addressed  (Tillmann et al. , 2018)  to decrease variability between raters and improve the results of a CAM. Additional criteria beyond those already included in RUCAM were proposed such as genetics, race, gender, or drug signatures, but the current knowledge on these factors is not strong enough in terms of sensitivity and specificity to be included and scored in any CAM. More prospective studies are needed to add new and validated criteria. The use of automated RUCAM should be encouraged in prospective and retrospective studies especially in large databases with thousands of potential DILI cases, as they exist in the form of EMR in hospitals  (Hunt, 2018)  or national health insurance registries. However, it should be used concurrently with strict DILI definitions and verification of the diagnosis in a sample of positive and negative cases.
RUCAM in Databases, DILI Registries, and Epidemiological Studies A study in the USA at Michigan University developed a novel text searching tool to identify DILI cases in EMR  (Heidemann et al. , 2017). RUCAM and EO were applied to suspected DILI cases with good agreement between these methods despite the fact that, in very few cases, the authors were unable to use RUCAM because of missing data on the time to onset of the liver injury. This so-called limitation of RUCAM is not substantiated because it is always possible to assign the lowest score to this criterion in absence of information  (Danan and Teschke, 2016). Furthermore, missing data are also an issue for the global introspection method, where it constitutes a limiting step unless assumptions are made that could also be used in RUCAM.
A study in Japan using RUCAM and a Japanese method DDW-J to build an algorithm for DILI detection in a medical information database showed an expected agreement between the methods since DDW-J derives from RUCAM and called for further studies on DILI in large EMR database  (Hanatani et al. , 2014). A meta-analysis of algorithms to identify DILI in EMR  (Tan et al. , 2018)  in 29 studies between 1993 and 2016 included causality assessment methods: EO in 16, RUCAM in 8 (starting in 2000), WHO in 1, RUCAM and WHO in 1, DDW-J in 1 and none in 2 studies. The positive predictive value of DILI detection algorithms calculated on 25 studies was low, ranging from 1. 0 to 40. 2%. These results were due to considerable variability in case definition of DILI, causality assessment methods, diagnostic codes, and study drugs.
CIOMS then RUCAM introduced liver test thresholds and liver injury classification before causality assessment criteria. RUCAM with its algorithm and scoring system provide easy tools for epidemiologists to ensure homogeneity and reliability of the results. Despite this advance in searching for tools in DILI, the LiverTox database has not systematically used RUCAM but preferably EO to analyze the published cases. In a study based on the original and the updated RUCAM, the quality of a sample of the LiverTox DILI cases was assessed. As a result, some cases included in this database are likely not DILI due to insufficient data quality  (Björnsson, 2016; Björnsson and Hoofnagle, 2016). Indeed, this issue was also discussed by others  (Real et al. , 2019; Teschke and Danan, 2018b)  asking for improvements to continue to rely on LiverTox as a worldwide known and popular database on DILI characteristics.
RUCAM is widely used in epidemiological studies for the calculation of DILI incidence rates and to rank drugs according to their hepatotoxicity. Interestingly, a study to estimate DILI incidence in EMR database jumped over CAMs to use only DILI definition and temporally related criteria  (Shin et al. , 2013). As the selected codes excluded alternative causes, it was thought that DILI cases were correctly assessed for causality. The diagnosis was based on the assumption that the codes were valid. This was not verified on a sample of cases and constitutes one of the weaknesses of this study. It would have been preferable to use RUCAM as strongly suggested to ascertain the diagnosis of DILI and would make the incidence rates, DILI characteristics, and other calculations more reliable  (Hunt, 2018).
Another example in a large hospital database is a case-control study designed to detect hepatotoxicity of new drugs and quantify the risk of DILI, RUCAM was used to search for cases and assess causality  (Douros et al. , 2014). Other RUCAM-based incidence studies performed before 2014 in various countries are summarized in a recent review  (García-Cortés et al. , 2018). Many epidemiological studies were based on RUCAM to identify DILI cases among which it is worth quoting: registries in Spain, Iceland, Latin America  (Bessone et al. , 2019) , or in hospital data bases in Korea in 2012, Mainland China  (Shen et al. , 2019) , Japan  (Aiso et al. , 2019) , Thailand  (Sobhonslidsuk et al. , 2016) , in a DILI cohort study for liver transplantation  (Baekdal et al. , 2017) , and in a prospective cohort study in India  (Rathi et al.
The reviewers, as experts, should pay attention to these cases that could enter in DILI databases only because they are published and not more rigorously on the basis of objective criteria and transparent method. Moreover, if the publication includes few cases, the detailed score of RUCAM has to be included in the article or in a supplementary material to ensure the transparency of the assessment. Despite these recommendations  (Danan and Teschke, 2018) , a number of articles are still published only on the basis of the author's opinion preventing a reassessment by the reviewer and the editor. Similarly, regulators should base their evaluation and decision on validated DILI cases assessed with RUCAM. Few national agencies require such assessment when drug hepatotoxicity is discussed after the first case(s) of acute liver injury in postmarketing or clinical trials in drug development.
With RUCAM, retrospective or prospective studies involving DILI will benefit for a harmonized approach. The language for international DILI studies could be provided by RUCAM. It would be of great scientific interest to gather the cases from databases in different countries. This cooperation would increase the number of cases and controls so that the power of the detection would increase in parallel as well as the identification of risk factors such as obesity, nonalcoholic fatty liver, diabetes mellitus, HLA variants, and genetic and serum biomarkers. Together with the preceding rule on the incorporation of "probable" and "highly probable" cases only would make significant progress in our knowledge on DILI.
RUCAM Improvements Responses to comments on the original version of RUCAM of 1993  (Hassan and Fontana, 2019)  were provided and already implemented in the updated version  (Danan and Teschke, 2016). Other comments were addressed in a summary table  (Danan and Teschke, 2018)  and in detailed work instructions (Electronic supplementary material in  Danan and Teschke, 2018). The introduction of new biomarkers and inclusion into RUCAM were specifically discussed  (Teschke et al. , 2017)  to focus on the method for the validation of new criteria. Another approach proposed by a DILI expert would be to combine RUCAM results with the DILI signature, i. e. , the clinical and biochemical characteristics, of a specific drug as provided in articles or in specialized database such as LiverTox  (Watkins, 2015).
Continuous logs for a better calibration of the seismic dataset To design horizontal wells in thin layers (limestone or sandstone), a good precision of converted seismic data in depth is required. Softwares for building a synthetic trace give this required precision if the borehole measurements are precise and continuous. In other words, to avoid a bad calibration of the seismic dataset and, consequently, a bad time-depth conversion of the 3D seismic tainted with high uncertainty, continuous logs are necessary on the whole length of the borehole for the Sonic and the Density. The aim is to obtain a synthetic seismogram without artefacts due to the lack of measurements at the casing shoes and changes in the diameter of the borehole. To do this, it is necessary to make a ''pocket'' of about 15 m (depending on the total length of the measurement tools and the position of the sensors on each tool) to have common measurements in both runs of logs.