dataesr/scientific-paragraphs-categorization huggingface dataset:
1 m deep) was formed by sealing the top of each column with a rubber stopper (t = 0 min) and gas samples (7 mL) were withdrawn at 0, 5, 10 and 20 min via a rubber septum placed at the side of the column half way down the headspace (Figure  1 ). Each sample was injected into a pre-evacuated 6-mL screw cap septum vial and the rubber stoppers were removed after gas collection. On days when gas collection coincided with irrigation of the columns (days 5, 8 and 12), samples were taken 1 h after water application. All samples were measured for CO 2 and CH 4 and the data were analysed by calculating the rate of change of CO 2 and CH 4 concentrations in the chamber headspace using linear regression. Fluxes (g m -2 h -1 ) were calculated  (Troy et al. , 2013)  as: Flux = ΔGas x V headspace x ρ gas 100 x Δt x A column (1) where: ΔGas / Δt = rate of change of gas concentration (% h -1 ); V headspace = headspace volume (m 3 ); ρ gas = gas density at operating temperature (g m -3 ), and A column = column surface area (m 2 ).
Data analysis Prior to analysis, all data were tested for normality and homogeneity of variance to ensure compliance with Gaussian distribution requirements. Differences in leachate flux and gas emissions were assessed using one-way ANOVA in SPSS (IBM SPSS Statistics 20 Core System). Statistical results were considered significant at α = 0. 05 and all differences discussed in the text are at this significance level. To identify the treatments that had the potential to cause the most environmental damage in terms of GHG emissions and leaching of nutrients and carbon, the cumulative TN and TC losses (kg ha -1 ) and the cumulative CO 2 and CH 4 losses (expressed as total equivalent CO 2 emissions in kg CO 2 -C ha -1 ) over the study duration were added together (after  Healy et al. , 2014). Although this method does not take into account legislative drivers which may emphasise potential groundwater impact over gaseous emissions in some countries, it serves to contextualise the study results and allows overall impact of each treatment to be estimated and compared to one another.
In this paper, we present a novel and complementary analysis in which we estimate surface buoyancy fluxes and their impacts on ocean surface water-masses, without the use of a complex numerical model, but rather directly from an unprecedented database of ocean measurements under sea-ice; combining observations from ships, autonomous floats, and animal-born sensors  13. Surface buoyancy fluxes received by the ocean surface mixed-layer from atmosphere, ice, and diapycnal mixing are inferred as a residual of observation-based mixed-layer heat and salt budgets (see Methods section, Eqs. (  4 ) and (  5 )), and watermass characteristics are derived from hydrographic observations  13.
The geographical pattern of buoyancy fluxes presents a largescale meridional gradient, with the largest fluxes near the Antarctic coast. This meridional gradient is largely dominated by the freshwater flux contribution (Fig. 2 ), and is more marked in winter than in summer. Even though we are unable to disentangle from our dataset the contribution of sea-ice, precipitation, and diapycnal mixing, sea-ice is likely a strong contributor to the freshwater flux  13. The meridional gradient in the buoyancy flux and its seasonal evolution is consistent with the fact that the largest amount of ice formation occurs near the Antarctic coast on continental shelves, while ice melt tends to be more spread out after ice has been exported away from its formation region by wind and currents  15. Water-mass transformation and related vertical circulation.
Based on the calculation in the Weddell Sea, we deduce that only 1 Sv of water denser than 27. 9γ subducts outside this region. We believe this value to be strongly underestimated due to the poor resolution of potentially key local and transient processes (e. g. , coastal processes and polynyas) in our dataset. Important buoyancy fluxes are not likely to be captured within the very dense layers, especially in regions of intense polynyas activity such as the East Antarctica and the Ross Sea (See Supplementary Note 5 and Supplementary Fig. 6 ). An alternative and complementary way to examine the variability of water-mass transformations within each density layer is to investigate transformation in temperature-salinity space, which allows us to distinguish some features that are overlooked when viewed only in neutral density space  28.
As shown in Eq. (2), two critical pieces of information are needed to compute annual-mean watermass transformation and formation. The first of these is the climatological seasonal cycle of potential density (σ) in the ocean surface mixed layer. The second is the climatological seasonal cycle of the diabatic flux, D, in the ocean surface mixed layer. In this paper we infer these two terms using a large database of hydrographic observations in the Southern Ocean sea-ice sector. We define the sea-ice sector as the region seasonally capped by sea-ice, i. e. , the region south of the winter (September) sea-ice extension with an ice concentration greater than 15%. The seasonal cycle of sea-ice extent is estimated using a climatological-mean (2000-2015)  satellite-derived observations from Nimbus-7 SMMR and DMSP SSMI/SSMIS passive microwave data at 25 km resolution.
The international Argo project revolutionized our knowledge of the oceans over its entire seasonal cycle and in the middle of the basins, far from repeat hydrography lines and coastal regions. In particular, in the historically poorly sampled Southern Ocean, the Argo project provided a detailed understanding of the mixed-layer and its density  42, 43. However, until recently, Argo floats were unable to sample in the sea-ice sector. In combination with the Argo program, and traditional ship based observations, Pellichero et al. 13  used observations from instrumented marine mammals  44  that widely cover the Southern Ocean sea-ice sector over its entire seasonal cycle. These complementary databases produced a robust climatology of Southern Ocean mixed-layer characteristics in the sea-ice sector, which we use in this study to compute an observation-based seasonal cycle of potential and neutral density in the Southern Ocean mixed-layer.
As such the water-mass transformation calculations are applied to the Southern Ocean mixedlayer which varies from tens of meters in summer to more than 200 m during winter (see Pellichero et al. 13  for more details). Surface diapycnal fluxes are composed of ocean buoyancy exchanges with the atmosphere and the cryosphere, as well as diapycnal mixing (both vertical and horizontal). Ocean buoyancy fluxes are commonly obtained from atmospheric reanalysis datasets. Unfortunately, in the Southern Ocean such reanalysis products are poorly constrained, and essentially unusable in the seasonally sea-ice covered region. Instead, we derive our own unique estimate of surface diapycnal fluxes product based on the climatological seasonal cycle of ocean surface layer characteristics under sea-ice  13. The mixed-layer heat and freshwater budget can be expressed as S m h m Á FWF þ R S ¼ ∂S m ∂t þ u e Á ∇S m þ u g :∇S m þ w e ΔS m h m ; ð4Þ and H ρ 0 C p h m þ R T ¼ ∂T m ∂t þ u e :∇T m þ u g :∇T m þ w e ΔT m h m ; ð5Þ where u e is Ekman velocity, u g is geostrophic velocity, S m and T m are respectively the mixed-layer absolute salinity and conservative temperature, h m is the mixedlayer depth, w e is the entrainment velocity, and R S and R T are respectively salinity and temperature diapycnal mixing in the ocean mixed layer (both lateral and vertical).
Acknowledgements This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon2020 research and innovation program  (grant agreement  637770 ). C. C. C. was funded by a  National Science Foundation Division of Ocean Sciences  Postdoctoral Fellowship number  1521508. S. M. D was supported by the  Australian Governments Business Cooperative Research Centres Programme  through the  Antarctic Climate and Ecosystems Cooperative Research Centre (ACE CRC ). The authors thank  A. Petty ,  A. Barthélemy ,  T. Tamura , and  A. Haumann  for providing the air-ice-sea data set. The authors also thank  R. Abernathey  for providing his water-mass transformation data set. Argo data were collected and made freely available by the  International Argo Program  and the national programs that contribute to it (http://www.
METHODS A cross sectional study was conducted at Aga Khan University (AKU) Karachi from October 2016 till August 2017. All students from year one to five of MBBS, year one to four of School of Nursing & Midwifery and year one to two of Diploma in dental hygiene who had attended at least six months of medical education on campus were recruited. Individuals who reported to be diagnosed with depression or taking treatment for any such condition were excluded from this study. The institutional ethical approval was obtained before data collection was commenced (Ref#4361-BBS-ERC-16). An email/SMS was circulated among the students informing about the study and its objectives. They were requested to sign a consent form if they agreed to participate in this research. They were informed that their participation was completely voluntary with a choice to remain anonymous.
In order to achieve a power of 95% with a 15% estimated prevalence of depression/anxiety and a two-sided 5% level of significance, the minimum sample size required was n=196 (http://www. openepi. com/Menu/OE_Menu. htm); however we were able to recruit n=283 students for this study. Data were stored and analyzed using IBM-SPSS version 23. 0, count and percentages were given for years of study, gender, and other qualitative parameters, mean and standard deviation were given for age, BMI, scores on scales (AKUADS and SSI) these scores were compared across Bachelor of Medicine and Bachelor of Surgery (MBBS), School of Nursing and Midwifery (SONAM) and Diploma in Dental Hygiene (DDH) students using one way ANOVA, and further post hoc analysis were done using Tukye's test. Scores were compared between gender using independent sample t-test, spearman rank correlation was done to see the relationship of these scores with other factors, Pearson chi square test was done to see the association across MBBS, SONAM and DDH students, p-values less than 0.
On the contrary, literature suggests that the prevalence of anxiety/depression increases proportionately throughout the academic years. 21, 22 A study conducted in Lahore emphasizes the fact that medical students feel stressed out due to lack of study-life balance, recreation, and sleep. These are attributed to the sense of competition and a large number of summative tests, fear of Noman Rehmani et al. Significant difference was observed between girls and boys in facing situations and responding to stressors as girls showed higher levels of stress as compared to boys. failure, scoring lower than hoped for, and high parental expectations. 13 Our study differs from the previously published data in the sense that it focused on a versatile group of students in terms of degree program i. e. different modalities of medical education and was not limited to MBBS students only.
Further, we not only assessed the prevalence but also were able to identify the stressors/ triggers and suggest valid coping mechanism/s. Most of the previous data used questionnaire related to depression in general, but we used an additional tool which is a specifically designed questionnaire for assessing stress in students. The Aga Khan University has a state of the art Sports and Recreation Centre (SRC) that provides an excellent opportunity for students to sweat out all the stress that piles up after a busy schedule. SRC also provides a platform for other extracurricular activities comprising of training classes like yoga, musical instruments, fitness challenge etc. In addition, the university encourages the student body to form committees for multiple activities each of which has a representative from every batch.
Ethical considerations Written informed consent was obtained from the parent or the guardian of each child enrolled. The MSF Ethical Review Board and the National Ethics Committee of the Ministry of Health of Niger approved the study protocol. The study was registered at Clinical-Trials. gov (NCT01755559). Of the 221 children included in each treatment arm, 4, 3 and 3 children were excluded from the Kaplan-Meier analysis in the AS-AQ, DHA-PPQ and AM-LM arms respectively, while, in the same order, 41, 63 and 86 children were excluded from the per-protocol analysis. The study flowchart, with the reasons for excluding from the analyses, is presented in Fig. 1.
Acknowledgements We would like to thank all children and their parents who consented to participate to the study. We acknowledge their patience and kindness. We also thank all the personnel of the  Epicentre Research Centre in Maradi , including the physician  Makara Djizo Roukia , the study assistant Mamaty Abdoul Aziz, the laboratory supervisors:  Christelle Wandjam  and  Aissatou Moumini , the laboratory technicians:  Yacouba Adamou-Sekou ,  Ibrahim Souley ,  Nana Mariama Ali  and  Rayanatou Sabiou-Mamane , the nurses:  Fatoumatou Ibrahim Alou ,  Mansour Arzika ,  Nafissatou Daouda-Morou ,  Adamou Habou Harouna ,  Aicha Laminou Abdou ,  Djamila Haya-Ali ,  Amina Souley ,  Kadidia Karimou  and  Razina Moussa , the nurse assistants:  Salissou Ibrahim ,  Rabiou Moussa  and  Halima Tidjani , the data manager  Smaila Gnegne  and the data clerks:  Laouali Guiri  and  Nouroudine Sani.
Availability of data and materials The datasets supporting the conclusions of this article are available on request, in accordance with the legal framework set forth by Médecins Sans Frontières (MSF) data sharing policy  [37]. MSF is committed to share and disseminate health data from its programmes and research in an open, timely, and transparent manner in order to promote health benefits for populations while respecting ethical and legal obligations towards patients, research participants and their communities. The MSF data sharing policy ensures that data will be available upon request to interested researchers while addressing all security, legal and ethical concerns. All readers may contact the generic address data. sharing@msf. org or Ms Aminata Ndiaye (Aminata. NDIAYE@ epicentre. msf.
Acknowledgements C. H. acknowledges the  Région Bretagne, France for funding. EZ-C acknowledges the University of St Andrews  and  EPSRC  ( EP/M02105X/1 ) for financial support. We thank  Umicore AG  for the gift of materials. We thank the  EPSRC UK National Mass Spectrometry Facility  at  Swansea University  for analytical services. This research used computational resources of 1) the  GENCI-CINES/IDRIS , 2) the  CCIPL (Centre de Calcul Intensif des Pays de Loire ), 3) a local Troy cluster. EAG and HVF thank the  ERC for a Starting Grant  (p-TYPE, 715354).
Thus, data with a recycle delay of 60 s could be considered as quantitative. 13 C solid-state NMR analyses were performed on a Varian VNMRS 600 MHz NMR spectrometer (B 0 = 14. 1 T, ν 0 ( 1 H) = 599. 8MHz and ν 0 ( 13 C) = 150. 8MHz). Experiments were performed using a Varian T3 HXY 3. 2 mm probe, tuned to 1 H and 13 C, and spinning at 18 kHz. A 1 H-13 C CPMAS (Cross Polarization Magic Angle Spinning) pulse sequence was used, with a 1 H 90 • excitation pulse of 2. 5 µs, followed by a ramped contact pulse of 2 ms. Spinal-64 1 H decoupling was applied during acquisition (100 kHz RF). The recycle delay was set to 1-2 s, depending on the sample, and the number of transients acquired ranged from 1430 to 7670. The 13 C chemical shifts were referenced to TMS (tetramethylsilane), using the deshielded resonance of adamantane (38.
The authors present and analyze measurements performed during a large accidental fire near the metropolitan area of Paris. Lidar measurements for such fresh smoke plumes not related with biomass are hard to find and thus such measurements are very useful to extend our database with optical and microphysical properties of certain type of smoke. Therefore the paper is a valuable contribution and suitable for publication in AMT. However the structure of the paper and its focus as written, do not highlight the truly new information shown and the presentation of the fire event could be drastically improved. The paper should be accepted for publication after considering my comments and suggestions below: We have changed the title, in order to highlight the "warehouse fire smoke". "Analysis of a warehouse fire smoke plume over Paris by N2-Raman lidar and optical thickness matching algorithm".
Lidar-derived aerosol optical properties in the low-middle troposphere Lidar-derived aerosol optical properties The lidar observations of the low troposphere following the accidental fire event are analysed using the TDAM approach. Lidar data is time averaged over 60 min to increase the SNR, especially for the N2-Raman channel. The reference zone of each profile is chosen below the thick stratus cloud. The TDAM algorithm is applied to the average profiles for the whole observation period to retrieve the aerosol optical parameters and mainly the vertical profile of the LR. The LR profiles are then used to invert the 1-min resolution elastic signal to assess the vertical profile of the AEC. As previously explained, the PDR is calculated only for AEC greater than 0. 01 km -1 , as it is undefined and noisy for lower values.
Acknowledgments This work was supported by the  Commissariat à l'Energie Atomique  et aux énergies alternatives (CEA). The authors would like to thank the  AIRPARIF network  for providing ground-level aerosol data (available at http://www. airparif. asso. fr/en/telechargement/telechargement-station), the  AERONET  network for sunphotometer products (at https://aeronet. gsfc. nasa. gov/). The authors acknowledge the MODIS Science, Processing and  Data Support Teams  for producing and providing MODIS data (at https://modis. gsfc. nasa. gov/data/dataprod/),and the  NASA Langley Research Center Atmospheric Sciences Data Center  for the data processing and distribution of CALIPSO products (level 4. 10, at https://eosweb. larc. nasa. gov/HORDERBIN/HTML_Start. cgi). The  NOAA Air Resources Laboratory (ARL)  is acknowledged for the provision of the HYSPLIT transport and dispersion model and READY website (http://www.
Magmas fracture under high shear stresses, producing radiating elastic waves. At the volcano scale, eruption is often preceded by accelerating seismicity, while at the laboratory scales, sample failure appears to be preceded by similarly accelerating Acoustic Emission (AE). In both cases, empirical relationships between the acceleration and the time of the singular final event have offered tantalizing possibilities for forecast of eruptions and material failure. We explore the success of these tools in the laboratory by briefly reviewing datasets that have been presented previously and comparing the range of errors on forecast times with the range of errors associated with attempts to retrospectively forecast eruptions. We demonstrate that the heterogeneity of a system is crucial to making accurate forecasts on the sample scale, such that homogeneous systems are inherently unpredictable.
We can see that the minimum error reported is as low as 0. 002 for the Redoubt eruption in  1989 -1990 (Voight and Cornelius, 1991)  and as high as 0. 36 for Pinatubo volcano erupting in 1991  (Bell et al. , 2013). In these two cases the values refer to the minimum and maximum differences between the forecast and the eruption. In general, there is little evidence in Table  1  that a particular volcano type, eruption style, or magma composition, results in a better predictability when using all the forecasts made. Rather, it seems more likely that variations on the error of any forecast is dominantly dependent on the placement and quality of instrumentation, the numerical forecasting technique applied (c. f. Bell et al. , 2011  for a discussion of techniques), and perhaps the nature of the seismicity (all data, or discriminated datasets from picking of specific event types).
As with the exponential model, it is not clear what use a linear model can be for forecasting critical events, but it nonetheless may be a reasonable descriptor of some datasets. For this, the log-likelihood is as follows  (Bell, pers. comm. ) ln(L) = n ln (c) -c(t n -t 0 ) (6) The best-fit ML parameters for each acceleration model are established by minimizing the negative of Equations (4-6). The observable typically is an acoustic or seismic event count, such that it is a pure number. For this reason, here we do not present the non-cumulative best-fit models graphically because they are not informative-the clarity comes in cumulative form where the data are effectively stacked and elevated into a line with a given curvature. Moreover, as we have the advantage of working with non-binned data, there is no use in looking at a timeline of event timings, which is what non-cumulative data amount to.
The cumulative form of the exponential model (corresponding to Equations 2 and 4) is (Bell, pers. comm. ) (t) = h q exp qt -exp (qt 0 ) (7) That of the TROL (corresponding to Equations 3 and 5) is  (Bell et al. , 2013) : (t) = k p -1 (t c -t) 1-p -(t c -t 0 ) 1-p (8) And that of the constant rate model (corresponding to Equation  6 ) is: (t) = c(t -t 0 ) (9) However, other metrics can be used such as strain, in the case of a constant driving-pressure scenario, or pressure, in the case of a constant displacement rate scenario. Other metrics that accelerate toward failure may exist. However, here we focus on event number as. The energy of acoustic signals cannot be used in the same way because the ML method relies on the event timings in the log-likelihood function. In what follows we will test each of these approaches against a range of experimental datasets.
The STA-LTA window was set to 1 and 20 ms, respectively and the STA/LTA threshold was 2. The amplitude in dB and energy (typically in nJ), of each event were computed based on a resistance reference standard of 10 k. All 42 samples were driven at constant rate as described above, until failure occurred where mechanical failure is defined as the point after which the sample is no longer load-bearing and the force drops to zero. This force-drop is easily picked in each mechanical dataset and provides excellent resolution on the measured t c , which can then be compared with the predicted t p using predictive tools described in section Quantitative Background.
FORECASTING THE FAILURE OF MULTIPHASE MAGMAS We take a staggered approach to data analysis. First, we consider that effects of material heterogeneity on forecast efficacy can be best determined by using the complete data set of acoustic emissions (section The Effect of Porosity). However, we acknowledge that a true "forecast" would only be useful if it can be made before the final critical failure event has been reached, and therefore using less than the complete dataset. Therefore, in a second step, we analyse the effect of taking an incomplete sequence of data on the efficacy of forecasts (section Hindcasting or Simulated Forecasting).
The Effect of Porosity Using datasets produced for deformation of sintered, variable porosity, variable grainsize, soda-lime silica glass beads  (Vasseur et al. , 2013 (Vasseur et al. , , 2015)) , and natural sintered Mt Meager volcanic rock  (Heap et al. , 2015; Vasseur et al. , 2017) , we can apply the techniques described above to test the efficacy of failure forecast tools. First, if we use 100% of the AE sequence, we can use the log-likelihoods given in Equations (4-6) to fit for the unknown constants in a linear form Equation (  6 ), an exponential form Equation (  4 ) and a power-law form Equation (5). In Figure  3  we FIGURE 2 | Example textures of variably porous sintered materials for which the pore space is rendered in 3-dimensions and colored gray if it is connected from edge-to-edge in any direction, or green if it is not.