dataesr/scientific-paragraphs-categorization huggingface dataset:
The majority of CTD casts were to a 200 m depth, with at least one 2000 m cast per station. Mixed layer depth was determined using  de Boyer Montégut et al. (2004) 's method, by finding the depth where density has changed more than 0. 3 kg m -3 from a reference value, which was chosen to be the value at a 10 m depth. The 10 m reference was A. de Verneil et al. : LD OUTPACE chosen because post-processed CTD casts did not always include the surface. The SedTrap Drifter had six SBE 37 Microcat CTDs on board. Their depths, as determined by mean in situ pressure, were ∼ 14,  55, 88, 105, 137, and 197  m. These instruments yielded data every 5 min during their deployments. As mentioned in the previous paragraph, during LDA the SedTrap Drifter tangled with the production line and so the data presented here from LDA came from its redeployment until the end of LDA.
Tracer analysis and baseline definition The need for a baseline within the OUTPACE dataset can be shown by comparison of the CTD data with climatologies such as the World Ocean Atlas (Fig. S1  in the Supplement;  Boyer et al. , 2013). While OUTPACE observations were consistent with these previous observations, when metrics of variability were available they produced envelopes of max/min T -S values large enough to preclude distinguishing between different stations. Since no other sufficiently fine data were available to compare T -S measurements, data from within each station were used to create a reference baseline of T -S variability. Given the lack of fine variability data and the need to work within the dataset of a single cruise, another approach was needed to condense T -S variability so that physical environments can be distinguished.
By compiling independent Z-scores over larger distances, we can test whether there is a relationship between Z-score and distance. During OUTPACE, complementary σ θ -spice observations stemmed from neighboring stations, the SD stations (Fig. 1 ). Additionally, surface measurements for the entire cruise were provided from a Seabird SBE 21 SeaCAT thermosalinograph (TSG), with SBE 38 thermometer using the ship's continuous surface water intake. Subsequent to post-cruise processing of TSG data as detailed in  Alory et al. (2015) , the time series was available in 2 min intervals. The relationship between Z-score vs. distance was used to evaluate the baseline. Distances were calculated from the ship position of observation and the initial CTD cast position for the LD station. For the SD stations, the Z-scores found by the functional fit of spice for each cast were binned by density, in a regular grid with bins of 0.
25 kg m -3 width. TSG data from during the LD stations were excluded and Z-scores were binned by distance from the station, in 10 km increments for the first 100 km, and then 20 km afterwards until 500 km. The spatial scale R Z was determined where Z ≥ 2 and used to evaluate the ability of the statistical baseline to discern gradients in physical properties. A natural spatial scale to serve as a useful reference to the empirical distance is the first Rossby radius of deformation, approximated via Wentzel-Kramers-Brillouin (WKB) method by  Chelton et al. (1998)  as follows: R D = 1 πf 0 -H N (z)dz, ( 2 ) where f is the local coriolis parameter and N (z) is the depthdependent Brunt-Väisälä frequency. R D was calculated for each LD station using the deepest cast available: 2000 m casts for LDA and B, and a deep 5000 m cast for LDC.
Currents analysis and Lagrangian risk The previous step analyzed the relationship between Z-score and distance, providing an estimated distance over which the physical environment changed. In order to evaluate the risk that the SedTrap drifter encountered different water masses, an analysis of the local currents was needed. Since it is clear that the SedTrap drifter was not perfectly Lagrangian and that vertical shear could transport layers at different rates, it was necessary to see if water at specific depths could have advected the distance over which different water masses appear. The in situ velocities for each LD station were derived from the shipboard acoustic Doppler current profilers (SADCP), two ocean surveyors at 150 and 38 kHz. Time series data for the SADCPs were post-processed using the CASCADE software package (Le  Bot et al.
, 2011; Lherminier et al. , 2007)  and binned into 2 min intervals. The 150 kHz SADCP provided a depth resolution of 8 m, with bins starting from 20 m, and reliable data coverage down to 200 m depth. Since the SedTrap Drifter had sediment traps extending down to 500 m depth, the 38 kHz data was also used, albeit with reduced depth resolution of 24 m bins, extending from 52 m down to 1000 m. Additional in situ velocities came from six Nortek AQUADOPP current meters, positioned at  11, 55, 88, 105, 135, and 198  m on the Sed-Trap Drifter. The post-processed AQUADOPP time series provided observations every 5 min. Velocities were integrated using a first-order Euler method to calculate the theoretical trajectories of water subsequent to the beginning of each LD station. Since the object of these calculations was to see whether water could have traveled a critical spatial scale, for each dataset the maximal amount of time was given for the time integration.
SADCP time series spanned between the first and last CTD of the LD station, using the ship position as the initial position. The AQUADOPP integrations spanned the entirety of valid data and used the corresponding SedTrap Drifter satellite fix for an initial position. To compare the integrated velocity positions with the realized positions of the SedTrap drifter and SVP drifters, GPS positioning was achieved by use of Iridium telemetry. Positions were successfully found for LDA before and after the SedTrap Drifter's redeployment, along with all of LDB. During LDC, the battery of the positioning antenna ran out and so the time series for LDC positions of the SedTrap Drifter was shortened. Since only the initial position is needed for the velocity integration, the AQUADOPP integration was continued beyond this positioning failure until the SedTrap Drifter was recovered.
Positions of the SVP drifters deployed at each station were successfully retrieved for all three LD stations. Satellite fixes were available spaced about 1 h apart for both datasets. Both SedTrap Drifter and SVP positions were interpolated to hourly time series. SVP positions were used to compute relative dispersion (Fig. S2  in the Supplement), using the definition for N particles as follows  (LaCasce, 2008) : D(t) = 1 2N (N -1) N i =j [x i (t) -x j (t)] 2 , ( 3 ) where N here is the total number of SVP drifters and x the time series of the drifter i's x, y position.
The time series of chl a and SST for the three stations are shown in Fig. 3. Comparing the three LD stations, a few patterns emerge. SST showed similar trends across the three LD stations. All stations experienced warming trends from December 2014 to mid-March 2015, consistent with summer heating. The lack of data from cloud cover sometimes led to abrupt drops in the distribution of daily SST shown. However, the timing of maximum temperature and the magnitude of that warming did differ between LD stations. A rapid heating in December 2014 occurred around LDA's position, which then slowly continued until early March 2015, at which point temperatures began to drop. Towards the end of sampling at station LDA the SST rises, possibly indicating another warming event occurred or the arrival of a warm patch of water.
Approaching 1000 km distance, |Z| > 2 occurred from the surface to 1024 kg m -3. By 3500 km, all density layers show |Z| > 2. LDB showed large Z-scores in some density layers at the closest SD station located 189 km away. Past 750 km, Z from 1022-1024 kg m -3 was consistently high. For densities greater than 1024 kg m -3 , Z-scores were enhanced between 1000 and 1500 km, but then decreased farther away. LDC's Z-scores show that |Z| was greater than 2 from the first observations at 310 km. All density layers showed enhanced Z values, with the majority of all observations being larger than 2. Similar to the TSG data, the SD stations showed that the baselines were sufficiently sensitive to detect physical gradients on large scales, with some detecting changes immediately. Putting together the near-surface TSG data and CTD data from the SD stations, LDA showed smaller |Z| = 2 scales at depth, whereas LDB and LDC both showed variability both near the surface and at depth at smaller scales.
The 38 kHZ SADCP velocities demonstrated that shallow depths flowed east like the SVP drifters, but with depth this advection swung to a more northerly direction (Fig. 15e ). The LDC SedTrap positioning was relatively uninformative, since the Iridium satellite fix was unavailable for the second half of the LD station and so showed little displacement (Fig. 15c ). The SVP drifters for LDC (Fig. 15i ), similar to LDB, underwent several oscillations and were advected the farthest, moving in a southeast direction. The SADCP data showed a shallow flow to the east, similar to the SVPs, but with depth the majority of trajectories oscillated near the station and even flowed southwest. For all the LD stations, the SedTrap Drifter stayed within a radius of R Z and R D centered at the LD starting position.
Integrated velocities of the SADCP also stay within R Z and R D , except for the trajectory nearest the surface for LDA. The SVP drifters for LDA and LDB also stayed within the R Z and R D distances, though the LDB SVP trajectories come close to R Z. For LDC, however, the SVP drifters traveled farther than R Z , but shorter than R D away from the initial position. R D was larger than R Z for all three stations. Since surface SADCP data for LDA crossed R Z , we evaluate that the water during LDA might be from a different water mass. Likewise, the SVP trajectories for LDC crossed R Z , meaning that surface water for LDC might be from a differ- ent water mass at the end of the station. As seen in the time series analysis, water at depth for LDC also changed part way through the station, though the SADCP velocities show little displacement.
, 2016). In that study, O 2 measurements were the distinguishing tracer, which we are precluded from using. As a result of all these factors, another quantitative method that works within the dataset of a single cruise in the WTSP is needed. The quantitative approach used in this study leverages the large quantity of in situ T -S data available from multiple platforms to condense the physical variability present in the WTSP during OUTPACE over a 4000 km distance during austral summer 2015. In order to do this, the statistical baseline in spice was defined (Fig. 5 ). In effect, as opposed to an absolute measure (i. e. , specific water mass determination), this provides a relative measure of variability that can be used. The time series analysis for the latter portion of the SedTrap drifter and CTD time series data showed density layers where variability was enhanced, and caution should be applied to the analysis of biogeochemical data.
Since the baseline is a relative measure, observations from outside the LD station were used to see at what scales physical gradients appear. The relationship between distance and variability (summarized as Z-scores) provided a method by which to establish this scale. Overall, variability increased with distance, as one would logically expect, but this was not monotonic across all datasets. Therefore, the first increase in Zscore above 2 (using an α = 0. 5 criterion) was used, and the smallest of these scales across datasets and density layers was conservatively chosen in determining the cut-off scale R Z. These distances were of the same order of magnitude as the Rossby radius R D. Despite the testing of the baseline on complementary data outside of LD sampling, there is still the question of its generality.
Clearly, at the surface, atmospheric forcing can influence the water's T -S (and spice) characteristics, and so will impact TSG measurements as well as observations in the upper mixed layer. Future applications of this method will have to take this variability into account, and perhaps make greater use of survey data to fill in the spice variability below the surface at these spatial scales. However, at depth the greatest source of along-isopycnal gradients in T -S, i. e. , density-compensated features, is mesoscale stirring  (Smith and Ferrari, 2009) , and so, generally, the assumption should be applicable. Part of our assumptions in developing this method requires that sampling is not near mesoscale fronts and eddies. However, their residual effects are the most likely to affect along-isopycnal variability in a given field campaign, and it is probably the reason that both Z-scores (especially near the surface) begin to increase and the R Z 's were found to be at or near the Rossby radius R D for each LD station.
The choice of spice as a variable, though useful, is not a magical transformation in itself. The similarity in T -S between LD stations is still manifest in spice-density space (Fig. 5 ), especially at a depth where some density layers overlap. This means that for these density ranges, the stations are not distinguishable from one another. The overlap in statistical baselines further emphasizes the need to compare with other datasets to highlight and determine the scales at which variability exists. Having enough data to span a sufficient spatial range is what will determine what differences in spice are relevant or not. The fact that the SedTrap drifter, positioned close to the CTD time series, mirrored the same trends, corroborated both the presence or lack of small-scale gradients shown through Z-score.
The existence of the sampling footprint requires biogeochemical investigators to re-consider a few assumptions. As stated in the introduction, this analysis uses only physical, and not biological, data. Therefore, one must ask whether horizontal gradients exist at smaller scales. The only way to characterize this is to make measurements through a gradient, though this can be difficult with the need to produce measurements over a large area within a short, quasi-synoptic timescale. Since there exist at least some trajectories for each LD station that approached R Z , the risk of encountering gradients is always present. Thus, for each biogeochemical measurement, the assumption of whether horizontal T -S gradients reflect biogeochemical gradients must be validated. For OUTPACE, use of a moving vessel profiler (MVP) to survey an extended area around each LD station was planned, but technical difficulties prevented their use in the application of this method, though the data were useful in another study  (de Verneil et al.
For example, particles sinking from the 1024. 5-1025 kg m -3 density layer during the later part of LDC will influence subsequent sediment trap collection. While water mass changes near the sediment trap depths are unlikely due to weaker currents, the example of LDC suggests the probability is non-negligible. In our study, since the CTD casts were focused on the upper 200 m of the water column, there were insufficient T -S measurements to conduct the time series analysis for all the sediment traps. We therefore cannot use this method to evaluate if the trap data reflect the same upper water column as observed during each LD station. In summation, the inability of the quasi-Lagrangian drifter to follow the water in a sheared flow requires the investigator to consider the spatial scale of horizontal gradients, which might be depth-dependent, and then to ask whether the measurement they are making involves integrating over timescales and depths that mean the measurements being made do not reflect the other in situ measurements.
Conclusions The methodology applied in analyzing the tracer data for OUTPACE is reflective of a few characteristics specific to this region and to biogeochemical datasets more generally. First, in an ideal situation, the T -S analysis would determine the component water masses. While this might be achievable for some well-studied regions of the World Ocean, this is not generally applicable. Second, alternative well-defined water mass properties, such as dissolved oxygen, tend to be applicable only at depth, such as through the thermocline. Naturally, this is because photosynthesis, respiration, airsea surface flux, and a panoply of other biogeochemical and physical processes occur near the surface and impact these tracers, making them not conservative. Unfortunately, we are precisely interested in analyzing the water properties of the surface ocean where these processes are most intense.
Then, a statistical baseline is created from the T -S variability seen during the station's occupation and transformed into spice-density coordinates. Comparison of independent T -S data to the baseline is used to calculate Z-scores, first to analyze the in situ time series of the drifter deployment, and then to establish a spatial scale R Z beyond which the T -S differences amount to a change in the physical environment. While this scale is depth-dependent due to vertical shear, for the OUTPACE cruise a conservative R Z applied to all depths was found to be close to the Rossby radius R D , in the 35-55 km range. The last step is to then use all available data regarding currents and drifter positions to evaluate whether any water parcel could have ostensibly traveled farther than R Z. During OUTPACE, some density layers were shown to be variable enough to represent a change in water mass, and some trajectories surpassed R Z.
Regarding the use of this methodology, we give a few recommendations for future cruise sampling: -Maximize use of remote sensing data during the cruise to identify possible mesoscale features to either avoid or sample inside of. This can be achieved with software such as SPASSO  (Petrenko et al. , 2017). -Upon arrival at the selected site, a deep CTD cast below the thermocline can be used to quickly calculate the local R D Rossby radius in real time and produce a rough estimate for maximum spatial scale. In patchier environments, this scale might be too large, and must be reduced. -Before and after each station, sample with a surveying instrument such as ISIIS, SWIMS, SeaSoar, or MVP beyond R D to get depth-resolved data at intermediate scales. -If possible, mount CTDs and current meters on the quasi-Lagrangian drifting array (perhaps a sediment trap that does not need to be removed constantly).
Acknowledgements. The authors would like to thank two anonymous reviewers and  Xavier Capet  for their insightful comments, and the editor Laurent Mémery. The authors would also like to thank  Gilles Rougier  for his help in acquiring and processing CTD, SVP drifter, and  SedTrap Drifter data. Marc Picheral  is thanked for his help with sampling and processing CTD data. Olivier Desprez de Géincourt  is also thanked for use of and processing of AQUADOPP and Microcat CTD data. Rick Lumpkin  and  Shaun Dolk  from  NOAA/AOML  are greatly thanked for providing and sharing SVP drifters and data. Satellite SST, chl a, and altimetry data have been provided by  CLS  in the framework of TOSCA/CNES funding; we warmly thank  Marie Isabelle Pujol  and  Guillaume Taburet  for their support in providing these data.
The authors are also indebted to the captain and crew of the  R/V L'Atalante  for their outstanding assistance and role in the OUTPACE cruise. This is a contribution of the  OUTPACE (Oligotrophy to UlTra-oligotrophy PACific Experiment ) project (Moutin and Bonnet, 2015) funded by the  French national research agency  ( ANR-14-CE01-0007-01 ), the  LEFE-CyBER program  ( CNRS-INSU) , the  GOPS program (IRD) ,  CNES  ( BC T23 ,  ZBC 4500048836 ), and the  European FEDER Fund  under project  1166-39417. The OUTPACE cruise was managed by  MIO (OSU Institut Pytheas, AMU)  from  Marseille (France). Catherine Schmechtig  is thanked for LEFE-CyBER database management. Aurelia Lozingot  is acknowledged for administrative aid for the  OUTPACE  project. Edited by: Laurent Mémery Reviewed by: two anonymous referees.
Data availability. All data and metadata are available at the French INSU/CNRS LEFE CYBER database (scientific coordinator: Hervé Claustre; data manager and webmaster: Catherine Schmechtig) at the following web address: http://www. obs-vlfr. fr/proof/php/ outpace/outpace. php (INSU/CNRS LEFE CYBER, 2017). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/bg-15-2125-2018-supplement.
The evolutionary history of the wolf-like canids of the genus Canis has been heavily debated, especially regarding the number of distinct species and their relationships at the population and species level  [1] [2] [3] [4] [5] [6]. We assembled a dataset of 48 resequenced genomes spanning all members of the genus Canis except the black-backed and side-striped jackals, encompassing the global diversity of seven extant canid lineages. This includes eight new genomes, including the first resequenced Ethiopian wolf (Canis simensis), one dhole (Cuon alpinus), two East African hunting dogs (Lycaon pictus), two Eurasian golden jackals (Canis aureus), and two Middle Eastern gray wolves (Canis lupus). The relationships between the Ethiopian wolf, African golden wolf, and golden jackal were resolved.
RESULTS AND DISCUSSION The genome dataset analyzed in this study contains 12 gray wolves and 14 dogs, chosen from regions overlapping the current ranges of the other basal canids included in this study, five coyotes, one Ethiopian wolf, three golden jackals, six African golden wolves (originally Canis anthus, but recently reclassified as Canis lupaster  [1] ), two dholes, four African hunting dogs, and one Andean fox (Lycalopex culpaeus) (Figure  1 ). Shortread sequencing of the samples and subsequent alignment to the recently published wolf genome assembly  [7]  resulted in genome-wide coverages ranging from 0. 6-26. 63 (for details, see Data S1). The genome-wide heterozygosity estimates (Figure  S1 ) clearly show reduced levels in the Ethiopian wolf, African hunting dog, and dhole, an observation that is consistent with their small population sizes.
The placement of the Ethiopian wolf as the basal group in this clade is consistent with tree topologies obtained in previous phylogenetic analyses based on concatenated gene sequences  [5]  and more recent multispecies coalescent analyses  [4]  of datasets consisting of a subset of exonic and intronic sequences, but differs from the topology based on concatenated analyses in the latter study. We note that this nuclear-DNA-based phylogeny also places dogs as a sister clade to European gray wolves. However, we caution that this placement has only moderate support (0. 86 mean local posterior probability); moreover, the gene tree quartet frequencies of alternate resolutions within the dog-gray wolf branches are comparable to that recovered in the main tree (Figure  2B , panel labeled  [20] [21] [22] , and thus no conclusion can be drawn about which wolf population gave rise to dogs.
Gray wolf reference genome  [7]  N/A Oligonucleotides AdapterRemoval2  [40]. Additionally, all paired end reads where the two reads overlapped by more than 10 base pairs were merged into a single read. Subsequently, the reads from each sample were mapped to the wolf reference genome  [7]  using bwa (v0. 7. 10; aln algorithm)  [41]. The mapped reads were filtered for PCR and optical duplicates using Picard (v1. 128, https://broadinstitute. github. io/ picard), and reads that mapped to multiple locations in the genome were excluded. GATK (v3. 3. 0) [42, 43]  was used to perform an indel realignment step to adjust for increased error rates at the end of short reads in the presence of indels. In the absence of a curated dataset of indels in wolves, this step relied on a set of indels identified in the specific sample being processed.