dataesr/scientific-paragraphs-categorization huggingface dataset:
3. 2-Carbon Incorporation versus ETR Carbon incorporation (P Chl ) was plotted against ETR Chl (Figure  2 ) to investigate the relationship between the carbon incorporation and ETR for each species, and to study the effect of nutrient treatments. Significant linear relationships were found for all species and all treatments (P,0. 0001). However R 2 values varied. The R 2 of the linear regression performed on the whole dataset of each species was always below 0. 800 due to the variability among treatments (Table  2 ). Moreover R 2 varied across species. Values were relatively low for A. glacialis, in particular under the N-lim and P-lim treatments, with values of 0. 384 and 0. 646 respectively (Table  2 ). By contrast, R 2 of the logarithmic regressions performed on the same data revealed relatively high values, i.
Acknowledgements Funding for this study was provided by  Fondecyt-Chile  (grants  1070964  and  1080468 ), Fondecyt grant PBCT Anillo  ACT 18  and a cooperative program  IRD/ Departamento de Geología, Universidad de Chile. Discussions and comments on earlier vesions of the manuscript by  C. Mpodozis ,  R. Charrier ,  M. Farías ,  G. Yáñez  and reviews by  A. Rapalini ,  M. Calvo  and  J. Skarmeta  greatly improved the original version of this article.
Similar pulse-like LP events can be seen at other time periods at Mt Etna and other volcanoes, when short-range observations are available (Fig. 1b , Supplementary Fig. S1 ). In fact, we were unable to identify long duration LP events at summit stations in these datasets amongst many thousands of pulse-like LPs. Hence, on several volcanoes, we have identified short duration pulse-like LP populations which masquerade as classical long-duration LP events if recorded > c. 500 -800 m from the epicentre. We conclude that the long seismogram durations in these data are purely path related. Based on waveform analysis, Harrington and Brodsky 14 arrived at the same conclusion for some hybrid volcano seismic events. A literature search reveals that shallow LPs are almost universally recorded at epicentral distances > 500 m on standard volcano networks, which usually lack summit stations for operational reasons.
Acknowledgements: Funding from  Science Foundation Ireland (SFI)  and the  European Commission , and computational support from the  Irish Centre for High End Computing (ICHEC) , are acknowledged. We are grateful to  M. Mora ,  J. Pacheco ,  F. Martini & G. Soto  ( Costa Rica )  Orlando Macedo & Adolfo Inza (Peru) ) and  Domenico Patanè  (D. P. ) and  INGV  staff (Italy) for field campaign support and  D. P. for feedback on an early manuscript. David Amitrano  is gratefully acknowledged for application of his damage mechanics code and  Aoife Braiden  for assistance with drafting the manuscript. Tom Eyre  is thanked for Supplementary Fig. S1. Three anonymous reviews helped improve the manuscript. *Correspondence to:  Christopher Bean ,  Seismology Lab. , School of Geological Sciences, University College Dublin, Belfield ,  Dublin 4, Ireland.
7 mm (Procavia, Dugong) and at the University Hospital Center of Montpellier MultiDetector CT scanner Lightspeed VCT (Lapeyronie Hospital, Montpellier, France) with a resolution of 350mm (Elephas). Image segmentation and 3D reconstructions of petrosals and bony labyrinths were done using Avizo 6. 3 (VSG) software. Measurements of bony labyrinths (Table  1 ) were taken using Avizo 6. 3 software. A summary of the measurement protocol is available in S3. In order to allow comparison with in-situ material, petrosals are described in their hypothetical in-situ orientation. On the contrary, orientation of the bony labyrinth was simplified: the anterior view of the bony labyrinth is orthogonal to the plane of the anterior duct, the posterior view is orthogonal to the plane of the posterior duct, and the lateral view is in line with the plane of the lateral duct.
Acknowledgments We are especially grateful to the authorities from the  Office National des Mines of Tunis , Kasserine district, and  Djebel Chambi National Park  which assisted fieldwork in the Chambi area. We thank  J. -L. Hartenberger  for fieldwork,  R. Lebrun  ( Montpellier Rio Imaging Platform ),  P. Taourel  and the  Medical Imaging Department team of the Lapeyronie Hospital (Montpellier)  for access to the CTscan facilities in Montpellier,  A. Currant ,  L. Howard  and  R. Garwood  ( British Museum of Natural History ) for access to the CTscan facility in London ( British Museum of Natural History ), and  C. Sagne  ( Museum national d'Histoire Naturelle, Paris ),  K. Samonds  ( Northern Illinois University ),  L. Waucheul ,  S. Vrard ,  B. Tabuce ,  B. Marandat  and  A. -L. Charruault  for their help.
LSCE implementation In the LSCE version of the setup, a 0. 2 ml droplet of ultrapure water is injected into a hydrophobic filter (M&C -LB-1SS) located upstream of the inlet to the Picarro system. Delivery pressure of the dry standard gas is about 0. 2 bar(g). As in the other methods, the experiment continues until the droplet is completely evaporated. Dry values are measured before the water droplet injection and are checked after the droplet evaporation. The first two to three minutes following the water injection are discarded for the final calculations. The filter may be heated to generate water vapor levels above the ambient dew point in the laboratory, even above 5 % water vapor. Experiments usually take 1-2 h. They are usually repeated three times to have sufficient experimental data points and to guarantee a more robust correction assessment.
Purge air for the Nafion is taken from the analyzer's vacuum line (reflux method). A target cylinder of dried compressed air is connected to the remaining port of the 3-way valve. The target cylinder is measured every 9. 5 h for 15 min. This Nafion based drying system achieves a water vapor mole fraction of approximately 0. 1 % (range 0. 04-0. 15 %) for the sample air. It also humidifies the air from the target cylinder to about the same mole fraction. The analyzer is calibrated against a set of WMO/CCL standards 4-5 times a year, at which time each cylinder is measured for 30 min. The target cylinder results are not used to adjust the data between calibrations. 5851 Figures.
Field calibrations were performed separately for each system, sampling NOAA-ESRL tanks for 10 min each every 23 h. The CO 2 only field standards for the CADS system were prepared by NOAA-ESRL (thus containing near atmospheric values of CO 2 isotopic ratios) and were calibrated at Penn State, while the field standards for the G2401 system were prepared and calibrated by NOAA-ESRL. The G2401 system also sampled an additional tank hourly to assess drifts in the CO measurement, and sampled 10 m and 40 m a. g. l. hourly as well. Data from the CADS system were subsampled according to when the G2401 data were available and hourly values were compared. Water vapor values ranged over a wide range during this period of time, even higher than 4 % at times. While these high levels give some reason to suspect that the inlet line upstream of both analyzers may have been contaminated with liquid water during the measurements, the fact that both instruments are sampling from the same inlet means that this potential contamination would not affect the headto-head comparison presented here.
5 • C), a solenoid manifold, and a 144 Nafion membrane dryer. The LI-COR baseline drift is monitored every 2 h, and a full calibration is performed twice per day. The flow rates through the two inlet systems were nearly the same, and a constant time offset was applied to account for different lag times through the 300 m long inlet line. The left panel of Fig. 25  shows the CO 2 time series for a typical 3 day period from both instruments. When the CRDS data stream is averaged over 30 s, the results become essentially indistinguishable on this graph. The right panel of Fig. 25  shows the difference between the two sets of measurements. The median difference between the two measurements over this period of time is 0. 042 ppm, with a standard deviation of 0. 056 ppm, well within the stated GAW compatibility goal of 0.
AMTD AMTD AMTD AMTD AMTD AMTD AMTD Acknowledgements. Laboratory  experiments at the  Max Planck Institute for Biogeochemistry  were supported through the IAGOS research infrastructure, funded in its preparatory phase under the European Community's  Seventh Framework Programme FP7 , grant agreement no. 212128. The measurement campaign at Pallas was supported by  MeteoSwiss  through engagement in the  WMO Global Atmosphere Watch Programme. A portion of the data from Mace Head observatory was provided thanks to  Michael Geever  and  Gerard Spain  from  National University of Ireland, Galway ; and thanks to  David Dood  from  Environment Protection Agency, Dublin, Ireland. Finally , the authors would also like to thank  R. Keeling ,  R. Weiss , and  L. Welp-Smith  for thoughtful discussion.
A total of 26 balloons with multiple radiosondes were flown during 16 clear nights. This paper presents preliminary findings from the analysis of all these data sets. Several classical Raman lidar calibration methods are evaluated which use either Vaisala RS92 measurements, point capacitive humidity measurements, or GPS integrated water vapour (IWV) measurements. A novel method proposed by Bosser et al. (  2010 ) is also tested. It consists in calibrating the lidar measure-ments during the GPS data processing. The methods achieve a repeatability of 4-5 %. Changes in the calibration factor of IGN Raman lidar are evidenced which are attributed to frequent optical re-alignments. When modelling and correcting the changes as a linear function of time, the precision of the calibration factors improves to 2-3 %.
Comparisons to CFH measurements showed that RS92 radiosondes have a small moist bias in the lower troposphere and a dry bias in the upper troposphere at night-time  (Miloshevich et al. , 2009; Nash et al. , 2011  Several studies also revealed that the night-time RS92 IWV measurements have a moist bias of ∼ 3 %  (Cady Pereira et al. , 2008; Bock and Nuret, 2009; Miloshevich et al. , 2009). More recent radiosondes intercomparisons indicate that the RS92 radiosondes accuracy is at the 3-5 %RH level  (Nash et al. , 2011; Vaisala, 2011a). The Vaisala Digicora III software used during DEMEVAP was version 3. 64. This software includes many of the bias corrections identified in earlier work  (Vaisala, 2011b)  and, hence, no additional bias correction was applied to the RS92 data used in this study.
(  1 )  (Bosser et al. , 2007). The minimal time bin is 5 min and the WVMR profiles are retrieved at 5 min intervals (hence introducing some correlation between profiles at upper levels). In the case of the OHP NDACC Raman lidar, a slightly different method for data selection is used, while a similar compromise is expected. An a priori analysis of the stationarity of the water vapour mixing ratio and cirrus clouds is performed to optimize the averaging period while the profile retrieval is not performed for integration periods smaller than 20 min  (Hoareau et al. , 2009). The minimal spatial resolution is 7. 5 m for the IGN-LATMOS lidar while the minimum is 75 m for OHP NDACC lidar. For both systems the vertical resolution degrades rapidly with altitude up to a maximum of 750 m in the upper troposphere.
During DEMEVAP, four Raman lidar calibration methods were compared: 1. Radiosonde profile matching: a scale factor f RS is estimated by a weighted least-squares method that minimizes the difference between the lidar WVMR profile (zenith pointing) and a reference radiosonde profile over a small layer. In the case of the IGN-LATMOS lidar, one calibration factor is estimated from each radiosonde and 20 min of lidar data (i. e. using four 5 min profiles) beginning just after the radiosonde launch. Several layers are tested. 2. Point humidity measurement matching from capacitive sensors: similar to the previous one except the scale factor f PTU is determined from slant measurements with the lidar pointing to one and the other 10 m masts. This method could only be tested with the IGN-LATMOS scanning Raman lidar.
One calibration factor is estimated for each slant lidar profile (integrated over 5 min) and the nearest-in-time PTU measurement (averaged over 10 min). Because of the presence of electrical interference in the short-range raw lidar measurements (until ∼ 120 m), the lidar data at the exact range of PTU1 (90 m) could not be used. Instead, the median measurement over 32 bins was used. The same procedure was adopted for PTU2 though less interference was observed at the exact distance (180 m). 3. IWV matching: a scale factor f IWV is estimated by comparing IWV estimates from GPS or radiosondes (IGN-LATMOS lidar) or SOPHIE spectrometer (NDACC-OHP lidar) to the Raman lidar WVMR profiles integrated over the portion of atmosphere were these measurements are reliable and completed below with radiosonde measurements and above with a standard atmospheric profile.
However, only zenith pointing water vapour profiles from the lidar are used in this method. During DEMEVAP, zenith pointing was not performed continuously over the nights because of the slant pointing needed to test method #2. Hence the results from this method were slightly noisier than during the VAPIC campaign  (Bosser et al. , 2010). The expected accuracy of these methods is limited by the accuracy of the calibration data (5 % for radiosonde data, 2 % for PTU data, and 2-5 % for GPS data) and by systematic and random errors in the lidar measurements.
Radiosonde data processing and humidity conversions In this study we used Vaisala RS92 data files delivered in "fledt" format and MODEM M2K2DC and M10 data in ". cor" format. The Vaisala measurements were available at 2 s (∼ 10 m) resolution and the MODEM measurements at 1 s (∼ 5 m). The P , T and RH measurements contained in these data files were taken as the best estimates as computed with the standard manufacturer's software. Only the Snow White data were reprocessed because the conversion of mirror temperature to RH depends on whether the condensate on the mirror is water or ice. Detection of the change from dew to frost is not automatic and requires a careful visual inspection of the measurements as described, e. g. by  Fujiwara et al. (2003). We used the Snow White housekeeping data (Peltier current and phototransistor voltage) to detect layers of discontinuity in condensate reflectivity and we compared the SW RH values for dew and frost to the RH values reported by the RS92 radiosonde onboard the same balloon.
Afterwards, all the profiles were checked manually by visual inspection and the upper portions of some profiles were removed when anomalous drifts or noise were detected. The radiosonde IWV contents were computed after the RH measurements were converted into specific humidity and integrated over pressure. Since all the radiosonde measurements were available at high resolution and had valid data from the surface up to the tropopause (10-12 km), no correction for missing data at the bottom or top of the profile had to be applied. Here, the numerical error in the IWV computation and representativeness error in the IWV intercomparison from distant instruments encountered in previous studies  (Bock et al. , 2005 (Bock et al. , , 2007 ) are expected to be negligible.
GPS data processing The GPS data were processed with the GIPSY/OASIS II v 6. 0. 2 software in Precise Point Positioning (PPP) mode  (Zumberge et al. , 1997). Phase measurements are decimated to a 5 min interval and data are analysed in a 30 h window centred on each day from which the 00:00-24:00 UTC parameters are extracted to avoid edge effects. We apply the IERS2010 recommendations for solid earth tides model (Petit and Luzum, 2010) and FES2004 model for the ocean tide loading effect  (Lyard et al. , 2006). Absolute antenna models (from igs. atx) are used for transmitters (satellites) and receivers. Phase ambiguities are fixed using the "ambigon" algorithm  (Bertiger et al. , 2010). The cut-off angle is fixed to 7 • without down-weighting of low-elevation observations and the uncertainty in phase observations is fixed to 10 mm.
Comparison of calibration methods with the IGN-LATMOS Raman lidar The four methods for calibrating WVMR lidar profiles (see Sect. 3. 1) are intercompared here using data from the IGN-LATMOS Raman lidar. Only Vaisala RS92 measurements are used for the methods relying on radiosonde data. The measurements are limited to the period 12 September-20 October: they exclude the first and last days because not all instruments were fully operational (e. g. GPS receivers). Figure  4a  shows a summary of results, with one example for each method except method #3 where two IWV references are tested (GPS and radiosonde). All five comparisons yield calibration factors around 1, revealing that the a priori estimate for the calibration coefficient is very realistic. However, there is a quite large degree of scatter in the results, both for a given method and between methods.
The mean values range between 0. 942 (GPS-lidar coupling) and 1. 010 (PTU2): disagreement is about 7 % on the calibration coefficient. The standard deviations of the values range between 0. 040 (∼ 4. 0 %) for IWV-GPS and 0. 057 (∼ 5. 7 %) for RS. The RS results for a layer and for IWV are fairly consistent (mean values of 1. 000 and 0. 977 and standard deviations of 5. 3 and 5. 7 %). This is true also for the GPS results (mean values differ by < 1 % and standard deviations are 4. 7 and 4. 8 %). However, the standard deviations found from these data sets are quite large. This is partly due to a drift that is clearly visible in Fig. 4a. All five comparisons reveal this drift which is actually contained in the lidar measurements. However, there is some uncertainty in the magnitude of the drift between methods (see the linear fit equations in Fig.
4a ). The values over the 45 days of the campaign range between -11 % and -18 % (slope value ×45). The N 2 calibration measurements in Fig. 4b  show a similar overall drift as well as short-term fluctuations that are highly correlated with the calibration factor variations. As discussed in Sect. 2. 1. 2, most of the variations in the calibration factor result from changes in the optical alignments and are of similar magnitude as seen in other Raman lidars  (Brocard et al. , 2012). Once the estimated calibration factors are corrected for a linear trend, the standard deviation is drastically decreased (by a factor of ∼ 2). The best fit is then obtained for IWV-GPS (2. 5 %) and the worst for PTU2 (3. 9 %). The calibration using IWV-GPS data is now proposed as the standard method for deriving accurate lidar profiles independent from radiosondes.
of the four methods. The radiosonde matching processed in a 1 km layer above the former one shows a decrease of 2. 3 % in the mean calibration factor and nearly a doubling of the standard deviation. This is explained by the larger errors in the lidar data at higher altitudes. The PTU matching using PTU1 instead of PTU2 shows a small change in mean value and a slightly larger standard deviation because of the interference in the lidar measurements at shorter range mentioned in Sect. 3. 1. The two IWV-RS solutions (IWV RS "daily" and IWV RS "all") differ in the number of points. The first solution provides a mean calibration factor for each night (i. e. , combining the measurements from the one or two soundings each night). The second solution provides the calibration factors for all individual soundings.
5 %RH up to 9 km which increases to 10 %RH at 10 km altitude and decreases above. In terms of relative difference, the values are even larger, with average bias of -19 % and -33 % between the surface and 12 km for M2K2DC and M10, respectively. The SW and   7 ). WVMR bias and standard deviation of difference (std. diff. ) are computed over a single profile. Two IWV solutions are compared for the OHP lidar: total column (reconstructed profile using RS + lidar + climate, see Fig. 3 ) and lidar range (2-8 km altitudes). For the IGN lidar, only one IWV comparison is reported over the lidar range (0. 85-6. 0 km altitudes). RP1 and RP2 refer to two profile reconstruction methods for the OHP lidar: RP1 uses Nîmes radiosonde data only and RP2 adjusts also the RP1 solution to fit the IWV measurements from SOPHIE spectrometer.
g. ,  Vaughan et al. , 1988; Sherlock et al. , 1999a; Leblanc and McDermid, 2008; Whiteman et al. , 2012). The use of external humidity reference measurements is a traditional approach which is inherently limited by the accuracy of the reference measurements. Hence it is important to simultaneously assess the quality of various candidate reference techniques and improve the calibration algorithms and methods. In this study, we compared four calibration methods and used three types of reference data (radiosonde data, ground-based capacitive humidity measurements, and GPS IWV and phase measurements) with redundancy in the data (four radiosonde systems, two capacitive humidity sensors and five GPS stations). Ground-based dew point measurements and upper air dew/frost point measurements provided by chilled-mirror hygrometers were used for validation.
This type of hygrometers is classified as a field calibration standard by  WMO (CIMO Guide, 2008). We used here these measurements to assess the absolute accuracy of our calibrated lidar measurements. Lidar calibration coefficients determined from Vaisala RS92 radiosonde measurements achieved a repeatability (shortterm stability or variability) of 2-3 % after a drift over the period of the experiment (1. 5 months) was subtracted. A consistent repeatability was also found using GPS IWV as a calibration measurement or the GPS-lidar coupled data processing method developed by  Bosser et al. (2010). Thanks to the pointing capability of the IGN-LATMOS scanning lidar, calibration from capacitive humidity measurements provided by ground-based sensors located at 90 and 180 m from the lidar could also be tested.
GPS is evidently the most convenient and most widely used technique since it is easy to deploy and maintain, and operates both at daytime and night. Though, the accuracy of GPS IWV is not well known. In this study we investigated some of the error sources inherent in the GPS measurements. Tropospheric modelling and multipath did not appear as being major error sources in the data that we analysed (Sect. 3. 3). The use of microwave absorbers did not change significantly (< 0. 3 kg m -2 ) the 5 min IWV estimates. The uncertainty associated with the use of different types of instruments and the variability from instrument to instrument of similar type were assessed by GPS to GPS comparisons and by comparing GPS to other instruments. The GPS to GPS differences were about ±0. 5 kg m -2 which are very likely due to the difference in the antenna types and antenna models used for the raw GPS data processing.
, 2007a; Leblanc et al. , 2011). The comparison with GPS measurements at daytime and night also poses the question of the diurnal variations of GPS errors. This study shows that mean biases in the range -0. 4 to 0 kg m -2 (-2. 9 to 0 %) could be achieved from daytime comparisons (GPS vs. AERONET and SAOZ) and -0. 1 to +0. 18 kg m -2 (-1. 1 to +1. 2 %) from night comparisons (IGN lidar and SOPHIE). These results are consistent with those reported by  Guerova et al. (2005) , who showed that GPS and microwave radiometers usually have constant bias through day and night. The main further perspective of this work is to reprocess and homogenize the IWV estimates from those remote sensing techniques that possess long-term databases for the study of climate trends and variability. Among these techniques, the astronomical spectrometers databases are able to retrieve historical values of H 2 O starting at the beginning of the 20th century.