dataesr/scientific-paragraphs-categorization huggingface dataset:
38 ± 0. 019 mmole N m -2 d -1 on days 3 and 5, respectively) and an increase in BP as described in Sect. We also examined relationships between T DIP and other biological fluxes using multiple regressions [logT DIP = f (log PP, log N 2 fix, log BP)], incorporating 91 samples for which the three rates were measured simultaneously (Table  4 ). The partial coefficients were significant for both N 2 fix (p < 0. 0001) and BP (p = 0. 003) but not for PP (p = 0. 23). As all biological rates decreased with depth, we also examined this correlation using data within the mixed layer to avoid the depth effect. With this restricted dataset (47 samples) the partial coefficients were significant only for BP (p = 0. 0024), just under the significance threshold for N 2 fix (p = 0. 056) and still non-significant for PP.
An overview of BP and PP fluxes in the WTSP Here, we provide a unique, coherent dataset with simultaneous estimates of PP, BP, T DIP and N 2 fix rates in the WTSP. Although recent interest has increased in describing fluxes and planktonic communities responsible for N 2 fix rates in diverse environments, particularly in oligotrophic open oceans, measurements in the tropical area of the South Pacific Ocean are rare (summarized in  Gruber et al. , 2016 , and more recently in  Bonnet et al. , 2017). Moreover, few studies have attempted to simultaneously study the consequences of such activities on the functioning of the microbial food webs. Stations in the western part of the transect along the Melanesian Archipelago (MA) generally displayed greater fluxes of PP, BP and N 2 fix rates than those in the WGY area, with a large degree of longitudinal variability.
12. 9 6. 3 6. 7 However, although an increasing number of PP and N 2 fix values are available in the WTSP and within the GY, the only other BP data available in these regions, to our knowledge, are those estimated during the BIOSOPE cruise (November/December 2004), along a longitudinal transect further east between Tahiti and Chile  (Van Wambeke et al. , 2008b). The authors measured BP integrated across the euphotic zone ranging from 86 to 144 mg C m -2 d -1 within the Marquesas Archipelago area, from 43 to 114 mg C m -2 d -1 within the center of the GY and from 57 to 93 mg C m -2 d -1 within the eastern part of the GY. Therefore, in the WTSP we encountered the same range of BP values as in the GY area east of 140 • W.
Including all direct and indirect estimates, the mean (±SD) BGE was 8 ± 4 % (n = 21). Although bias introduced when converting hourly to daily BP rates was not studied here, we used a dataset obtained in the South Pacific Gyre  (Van Wambeke et al. , 2008)  to estimate conversion errors. During the BIOSOPE cruise, vertical profiles of BP were acquired every 3 up to 72 h, using the leucine technique within the euphotic zone at three selected sites using a Lagrangian sampling strategy. For the three series of profiles, standard deviations of BPI with time were 13 % (n = 13), 16 % (n = 16) and 19 % (n = 9). Thus, standard errors represented 3. 6, 4. 2 and 6. 1 % of the mean BPI, respectively. We used the average value of this percentage (5 %) to estimate the bias introduced by the conversion from hourly to daily BPI estimates of the OUPACE cruise.
Figure9. Enrichment experiments. Initial conditions illustrated by vertical profiles (0-200 m) of in vivo fluorescence, BP, nutrients (nitrate (NO 3 ), nitrite (NO 2 ), and phosphate (DIP)) and enrichment factors sampled from the 12:00 ctd cast on day 2 of occupation at each LD site. As DOC was not sampled on this cast, we showed the data from all the other casts at the corresponding LD site (circles) and the average profile (line). Enrichment factors are the ratio of BP after a given enrichment (DIP: P in red; nitrate + ammonium: N in green; glucose: G in blue; and all components: NPG in black) compared to the unamended control, both measured after 24 h incubations. The error bar is standard deviation within triplicates, and a bar is shown only if BP is significantly greater than in the control (Mann-Whitney test, p < 0.
Acknowledgements. We thank  Sandra Helias ,  Olivier Grosso , and  Mathieu Caffin  for their support in providing nutrient and N 2 fixation data,  Nicholas Bock  for linguistic check,  David Nérini  for discussions on statistics,  Sophie Bonnet  and  Mar Benavides  for discussions on diazotrophs,  María Aranguren-Gassis  and an anonymous reviewer for their constructive advices to improve the manuscript. This is a contribution of the  OUTPACE (Oligotrophy from Ultra-oligoTrophy PACific Experiment ) project (https://outpace. mio. univ-amu. fr/,last access:  27 April 2018 ) funded by the  French research national agency  ( ANR-14-CE01-0007-01 ), the  LEFECyBER program (CNRS-INSU) , the  GOPS program (IRD) , the  CNES  ( BC T23 ,  ZBC 4500048836 ) and the.
Method Study Context and Sites The data were collected as part of a larger project (www. espa-spaces. org) working to establish how marine ecosystem services contributed to human well-being and poverty alleviation in coastal communities in Kenya and northern Mozambique. The study was conducted in 4 sites in Kenya and 4 in Mozambique adjacent to mangrove or coral ecosystems in rural and urban areas (more information on each site is available from www. espa-spaces. org). Community profiles were developed for each site based on secondary sources, participatory observation, and key-informant interviews. This work identified characteristics of each site and the main livelihood activities, in particular those related to the environment. Urban sites (Kongowea in Mombasa, Kenya, or and Maringanha, a suburb of the city of Pemba in Mozambique) had larger population sizes than other sites and a wider array of livelihood activities.
Our approach can be used to assess multidimensional deprivation but cannot be used to attribute deprivation to particular causes such as conservation interventions. However, conservationists could adopt or supplement the method to monitor the effects of specific actions. General improvements in welfare may also lead to reevaluations as to what constitutes meeting a basic need, thus shifting thresholds of harm over time. Despite the potential for thresholds to be context and time specific, our data showed a surprising consistency of thresholds across a range of urban to rural sites in 2 countries, suggesting that thresholds of harm in meeting the most basic needs are relatively consistent across different contexts--even if aspirations may be different in different sites. This supports the use of thresholds as an indicator of deprivation, but they should not be uncritically used over long or transformative periods.
Ice velocity is one of the products associated with the Ice Sheets Essential Climate Variable. This paper describes the intercomparison and validation of ice-velocity measurements carried out by several international research groups within the European Space Agency Greenland Ice Sheet Climate Change Initiative project, based on space-borne Synthetic Aperture Radar (SAR) data. The goal of this activity was to survey the best SAR-based measurement and error characterization approaches currently in practice. To this end, four experiments were carried out, related to different processing techniques and scenarios, namely differential SAR interferometry, multi aperture SAR interferometry and offset-tracking of incoherent as well as of partially-coherent data. For each task, participants were provided with common datasets covering areas located on the Greenland ice-sheet margin and asked to provide mean velocity maps, quality characterization and a description of processing algorithms and parameters.
The currently available ice-sheet velocity products consist of continental-scale seasonal mean-velocity maps and time-series of the horizontal ice-motion components for selected areas. Generation of the latter based on SAR data, which are the focus of this study, entails post-processing and mosaicking measurements of satellite line-of-sight (LoS) and flight-path (azimuth) velocity components, derived independently for a large number of radar tracks. This paper describes an activity carried out within the ESA Greenland Ice Sheet CCI project, referred to as the "ice velocity round-robin algorithm intercomparison", in which several international research groups were invited to take part in four experimental tasks, related to different measurement techniques/scenarios. Participants were provided with common SAR datasets, and asked to apply their processing algorithm of choice to derive ice-velocity measurements, which were then intercompared and validated against GPS data by the project consortium.
The measurement techniques considered in this study are reviewed in Section 2. 1, whereas the SAR and the validation datasets are described in Sections 2. 2 and 2. 3, respectively. In Section 3, the results provided by the participants, their inter-comparison and their validation are discussed for each experimental task. The impact of several algorithmic choices on the derived ice-velocity products is discussed in Section 4, and recommendations based on the best-performing approaches are provided. Conclusions are drawn in Section 5.
Offset-tracking methods measure both the LoS and the azimuth components of the displacement between two SAR acquisitions by estimating the mis-registration of corresponding points on ground due to motion. This is achieved by tracking incoherent amplitude or intensity features (incoherent offset-tracking) and/or coherent SAR speckle (coherent offset-tracking) in several ways. Amplitude or intensity cross-correlation (ACC and ICC in the following)  [10] , as well as translation estimation in the Fourier domain via phase correlation  [11] , are able to exploit both the presence of features and of correlated speckle, typically yielding results on the featureless, but possibly coherent, ice-sheet interior as well as on the crevassed, but often incoherent, glacier tongues. Improvements in terms of accuracy and spatial resolution can in some cases be obtained using coherence maximization  [12]  or complex cross-correlation (CCC)  [13, 14] , although these methods are only applicable in practice to areas/datasets in which a high level of phase coherence is retained.
Experiment Description The ice velocity round-robin algorithm inter-comparison experiment was organized into four different tasks, namely DInSAR (Task 1), MAI (Task 2), incoherent offset-tracking (Task 3), and partially-coherent offset-tracking (Task 4). Invitations to participate in one or more tasks were issued by the Greenland Ice Sheet CCI consortium to academic, research and business organizations, through announcements on the project website and on the CRYOLIST e-mailing list for snow and ice related research, as well as through personal communications. For each task participants were provided with the same input SAR and auxiliary datasets (Tables  1  and 2 ) and were asked to apply their preferred implementation of one of the three processing algorithms described in Section 2. 1. A set of deliverables was requested for each task (Table  2 ) and participants were requested to fill in a feedback form providing information on their processing algorithm and parameters.
The Differential SAR Interferometry dataset (Task 1) consisted of two European Remote Sensing (ERS)-Tandem pairs (I1 and I2 in Table  1 ) covering an area of about 200 km × 100 km (two standard frames) around the Storstrømmen glacier in northeastern Greenland (Figure  1b ). Precise state vectors from Delft University of Technology and the 90 m posting Greenland Mapping Project (GIMP) DEM distributed by the Byrd Polar and Climate Research center  [21]  were also provided. Both SAR image pairs are highly coherent due to the small spatio-temporal baselines (Figure  2a ,b), except for an unphysical band of lower coherence for the I2 pair, due to missing lines in the raw data. Participants were asked to generate a LoS velocity map using one of two applicable DInSAR techniques, namely DEME and DD, and to provide quality parameters if applicable as well as the position of any GCPs used in the processing.
The Multi Aperture Interferometry dataset (Task 2) consisted of the previously mentioned I2 pair in Table  1. Users were provided with the Level-0 (raw) data products and with the same ancillary data as for Task 1. Participants were asked to generate an azimuth velocity map and supply any applicable quality parameters and GCP positions. and covering an area of about 300 km × 70 km, featuring several large tidal outlet glaciers, such as Rink Isbrae, Store Gletscher and Sermeq Avannarleq (Figure  1d ). The Synthetic Aperture Radar raw data and the GIMP DEM were provided to the participants. Due to the longer radar wavelength, the I4 pair shows several patches of relatively high coherence values, both on the bedrock outcrops and on slowly-moving areas on the ice sheet (Figure  2d ). Coherence is, however, also lost for this pair closer to the ice margin, and in particular on the tongues of the above-mentioned glaciers.
For both offset-tracking tasks, participants were asked to generate LoS and azimuth velocity maps using their offset-tracking method of choice, and to provide any applicable quality parameters and the position of any GCPs used during the processing. 1. For the offset-tracking experiments (Tasks 3 and 4) image pairs I3 and I4 in Table  1  were provided. The I3 dataset consisted of an Environmental Satellite Advanced Synthetic Aperture Radar (ENVISAT ASAR) C-band image pair spanning two standard frames (about 200 km × 100 km) around the Upernavik Isstrøm glaciers in northwestern Greenland (Figure  1c ). The Synthetic Aperture Radar raw data were made available as well as Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) precise state vectors and the GIMP DEM. Due to the C-band radar frequency and the relatively long temporal baseline, the I3 pair has very low coherence values on the ice sheet (Figure  2c ).
The I4 dataset consisted of an Advanced Land Observing Satellite 1 Phased Array L-band Synthetic Aperture Radar (ALOS PALSAR) Fine Beam Single image pair, spanning 5 standard frames and covering an area of about 300 km × 70 km, featuring several large tidal outlet glaciers, such as Rink Isbrae, Store Gletscher and Sermeq Avannarleq (Figure  1d ). The Synthetic Aperture Radar raw data and the GIMP DEM were provided to the participants. Due to the longer radar wavelength, the I4 pair shows several patches of relatively high coherence values, both on the bedrock outcrops and on slowly-moving areas on the ice sheet (Figure  2d ). Coherence is, however, also lost for this pair closer to the ice margin, and in particular on the tongues of the above-mentioned glaciers. For both offset-tracking tasks, participants were asked to generate LoS and azimuth velocity maps using their offset-tracking method of choice, and to provide any applicable quality parameters and the position of any GCPs used during the processing.
Validation Strategy The SAR-based measurements provided by the participants were validated against in situ GPS measurements available from different sources. GPS coordinates and Cartesian velocity components are provided in Table  S1 , whereas their locations are shown in Figures  1  and 2. Neither the GPS measurements nor their locations were made available to the participants beforehand. For Tasks 1 and 2, velocities were derived from the positions of 10 stakes drilled into the ice surface, which were surveyed at least twice between 1992 and 1995  [22]. Since the last observations were in late June 1995, and thus between 6 and 8 months before the acquisition of the I1 and I2 SAR datasets (Table  1 ), the measurements were linearly extrapolated to a reference date  (1 February 1996) , to account for the post-surge, long-term adjustment of the ice dynamics, as discussed in  [22].
For Task 3, three measurement sources were used, namely stations deployed during the Ice2sea project between August 2009 and July 2011 (UPE2, UPE3, UPE4)  [23] , Automatic Weather Stations deployed in August 2009 within the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) project (UPE_U, UPE_L)  [24] , and a GPS network deployed during a field campaign in summer 2010 by DTU (UPF1-3, UPM1-3). For Task 4, GPS stations AVA1, RNK2, STO4 were used, which were deployed respectively on Sermeq Avannarleq, Rink Isbrae and Store Gletscher within the Ice2sea project between August 2009 and August 2010  [23]. For Tasks 1 and 2, velocities were derived from the positions of 10 stakes drilled into the ice surface, which were surveyed at least twice between 1992 and 1995  [22]. Since the last observations were in late June 1995, and thus between 6 and 8 months before the acquisition of the I1 and I2 SAR datasets (Table  1 ), the measurements were linearly extrapolated to a reference date  (1 February 1996) , to account for the post-surge, long-term adjustment of the ice dynamics, as discussed in  [22].
Measurements For this task, results were provided by 6 groups, affiliated (in alphabetical order) to the Geological Survey of Denmark and Greenland (GEUS), the Italian National Institute for Geophysics and Volcanology (INGV) and the Technical University of Denmark (DTU). Some institutions provided more than one result, obtained with different processing algorithms. Image focusing was carried out by all groups with the software of  [25] , whereas the interferometric processing chains, which were used were based on different combinations of the software modules developed by  [25]  and on DTU's IPP processor  [26]. The LoS mean-velocity maps generated by the participants are shown in Figure  3. Group 1 applied a DEME approach to the I1 dataset (Table  1 ), whereas groups 2, 3, and 5 applied the same approach to I2.
Measurements For this task, the I2 dataset in Table  1  was processed by 4 groups, applying different processing chains listed in alphabetical order: DTU  [34] , Massachussets Institute of Technology  [15] , University of Edinburgh  [18] , and the University of Seoul  [35]. Image focusing was carried out with the software of Werner et al. (  [25] ) and that of  Rosen et al. ([36] ). The resulting azimuth mean-velocity maps are shown in Figure  6. Multi-aperture processing was carried out by all groups by creating two azimuth sub-apertures for each acquisition, with frequency separations between 0. 4 and 0. 5 of the SAR Pulse Repetition Frequency (Table  5 ). Different approaches were followed to generate the sub-apertures. Group 1 focused each image by processing the full azimuth bandwidth, and subsequently carried out spectral whitening combined with azimuth common-band filtering and range spectral-shift filtering.
Co-registration was performed according to the two-step procedure described in Section 3. 1. 1. Finally, the two azimuth sub-apertures were formed by azimuth band-pass filtering. In contrast, groups 2 to 4 carried out the focusing step twice for each raw dataset, selecting different azimuth center frequencies and processing bandwidths to jointly carry out azimuth common-band filtering and sub-aperture formation. The corresponding sub-aperture images for each acquisition were then co-registered following a similar two-step procedure as for group 1. All groups performed low-pass filtering and decimation (multi-looking), followed by a local mean filtering in the case of groups 2 and 3 and by a Goldstein adaptive filter  [27]  in the case of groups 1 and 4. Group 4 carried out multi-looking and adaptive filtering on each sub-band interferogram, prior to MAI (difference) interferogram formation, whereas group 1 applied both filtering steps to the final MAI interferogram alone.
Measurements For this task the ENVISAT ASAR I3 dataset in Table  1  was processed by 6 groups, applying different processing chains, listed in alphabetical order: DTU  [39] , Environmental Earth Observation Information Technology (ENVEO,  [3] ), GAMMA-RS  [25] , German Research Centre for Geosciences (GFZ-Potsdam,  [40] ), GEUS  [25] , Swansea University  [41]. The raw SAR data were focused with the software of  [25, 36, 41]. LoS and azimuth mean-velocity maps were provided by all participants and are shown in Figures  9  and 10  respectively. The core processing algorithm was ICC for all groups, although several differences can be pointed out, as summarized in Table  7. Prior to offset-tracking, three groups carried out image co-registration, following the two-step approach described in Section 3.
To compare the location and spatial-correlation of the differences between groups, the received results were resampled to a common grid with a posting of 9 arcsec in latitude and 27 arcsec in longitude, corresponding to about 250 m × 250 m on ground. In Figures  12  and 13       Differences are consistently higher for the azimuth measurements, probably due to the fact that for this dataset the highest velocity gradients, and hence challenges for the measurement algorithms, occur in this direction. Several spatially correlated differences are found in shear zones and more in general in areas adjacent to data voids. The fact that this holds also for participants, such as groups 1 and 6, whose measurements have the same spatial resolution and level of filtering, and also similar outlier culling thresholds (Table  7 ), suggests that this is mostly due to different hole-filling procedures.
2, which would equally affect measurements on bedrock and at the GPS locations. A possible explanation could be that the limited (kilometric) spatial resolution of the SAR measurements (Table  7 ) has a greater impact on the Differences are consistently higher for the azimuth measurements, probably due to the fact that for this dataset the highest velocity gradients, and hence challenges for the measurement algorithms, occur in this direction. Several spatially correlated differences are found in shear zones and more in general in areas adjacent to data voids. The fact that this holds also for participants, such as groups 1 and 6, whose measurements have the same spatial resolution and level of filtering, and also similar outlier culling thresholds (Table  7 ), suggests that this is mostly due to different hole-filling procedures.
Concerning the agreement between validation results and quality parameters, for groups 1 and 6, almost all observed errors fall within two predicted error standard deviations (Table  S4 ). This is consistent with the previously mentioned hypothesis that the main error source for this dataset is the spatial variability of the underlying velocity field, particularly in the azimuth direction. The error predictions do in fact account for spatial variability, although just for the one between neighboring measurements, rather than for that within the coverage of the cross-correlation windows. For the groups which provided maps of NCC coefficient amplitude or cross-correlation SNR as quality parameters, there is not a clear relation between these and the observed errors (Table  S4 ). Remote Sens.
Concerning the agreement between validation results and quality parameters, for groups 1 and 6, almost all observed errors fall within two predicted error standard deviations (Table  S4 ). This is consistent with the previously mentioned hypothesis that the main error source for this dataset is the spatial variability of the underlying velocity field, particularly in the azimuth direction. The error predictions do in fact account for spatial variability, although just for the one between neighboring measurements, rather than for that within the coverage of the cross-correlation windows. For the groups which provided maps of NCC coefficient amplitude or cross-correlation SNR as quality parameters, there is not a clear relation between these and the observed errors (Table  S4 ).