dataesr/scientific-paragraphs-categorization huggingface dataset:
7 g cm -3 ) is the density of CaCO 3 ; and f CY and f Sh are the volume fraction occupied by water in the cell and shield, respectively. Equation (  7 ) assumes that cell volume scales linearly with cellular carbon content. This assumption is reasonable for coccolithophores due to the absence of large vacuoles  (Paasche, 1967). I used Eq. (  7 ) to calculate the diameter of the cell and the coccosphere for all the experiments in the database for which POC and PIC data are available (Fig. 2 ). The unknowns in this equation are d POM , f CY and f SH. First, d POM was set to 1. 5 g cm -3 , which lies at the centre of the range of values proposed by Walsby and Raynolds (1980; 1. 3-1. 7 g cm -3 ). Then f CY and f SH were varied so that the resulting diameter of the great majority of E. huxleyi spheres fell in the range 3-7.
huxleyi cells regulate their calcification rates/division rates in order to maintain at least a complete layer of coccoliths, even in growth-limited conditions  (Paasche, 1999). With these parameter settings, the resulting density of the naked E. huxleyi cell is 0. 18 pgC µm -3 , which is comparable to that of carbon in protist plankton of similar size determined by  Menden-Deuer and Lessard (2000). The cell diameter obtained with this procedure is compared with that obtained applying an existing relation between POC and cell volume  (Montagnes et al. , 1994)  in Appendix A1. The calculated coccosphere diameter of E. huxleyi is compared to the measured coccosphere diameter for the experiments in the database where POC, PIC and cell-size data are reported (Fig. 2b ). Although a clear positive relation between measured and calculated coccosphere size exists, the calculated diameters are always larger than the measured diameters (except for two experiments in  Kaffes et al.
, 2010). The large majority of coccosphere-size measurements in the database were carried out with Coulter counters, which often do not include the coccolith shield in the size measurement  (Iglesias-Rodriguez et al. , 2008; Oviedo et al. , 2014; van Rijssel and Gieskes, 2002). Consistently, the Coulter counter diameter for E. huxleyi corresponds to the cell diameter calculated with Eq. (7) (Fig. 2b ). Another reason of the observed discrepancy is the fact that in some experiments cells are fixed chemically prior to size measurements, a treatment that induces cell shrinkage. Appendix A1 discusses the discrepancy between measured and calculated coccosphere size more in detail. With these considerations in mind, the choice made above of constraining Eq. (  7 ) with the range of E. huxleyi coccosphere diameters measured with the microscope  (Henderiks, 2008)  appears to be the safest.
2d ). However, the discrepancy is small for these larger-sized species. Significantly, the coccosphere diameter of Calcidiscus leptoporus measured with SEM without prior fixing of cells by  Langer et al. (2006)  coincides with the calculated coccosphere diameter using Eq. (  7 ) (Fig. 2d ). When discussing cell and coccosphere size from experiments in the database I use Eq. (  7 ) throughout the rest of this manuscript, regardless of whether size measurements are reported in the literature sources or not.
The allometric scaling of coccolithophore metabolism In this section the coccolithophore database is used to investigate relationships between cell volume and metabolic rates across different taxa under comparable growth conditions (allometric relations). The differences in metabolic rates we will deal with are largely due to differences in characteristic cell size across different taxa. Allometric relationships for coccolithophores will be compared with similar relations for other phytoplankton groups compiled by  Marañón (2008). The  Marañón (2008)  data set includes cell volume and metabolic rate data measured in the field for a vast array of unicellular photosynthetic organisms spanning 9 orders of magnitude in size, from photosynthetic cyanobacteria (volume = 0. 1 µm 3 ) to large diatoms (volume = 10 8 µm 3 ) and including dinoflagellates and haptophytes.
The  Marañón (2008)  data set reports rate measurements that mostly reflect in situ optimum growth conditions; thus, in this section, I focus on experiments in the coccolithophore database that were carried out in optimum conditions (Table  2 ). The assumptions made in comparing metabolic rates from the coccolithophore database with those measured in the field by  Marañón (2008)  are detailed in Appendix A2. Figure  3a  and b compare the allometric relations of photosynthesis and growth for coccolithophores with those established by  Marañón (2008)  for phytoplankton. Figure  3c  and d  show the allometric relations for photosynthesis and calcification in coccolithophores, highlighting the position of the five different coccolithophore species considered. Linear regressions through the optimum coccolithophore data set yield the following equations: log 10 (RPh i ) = 0.
The finding of a near-isometric scaling of coccolithophore growth in laboratory experiments has implications for the  ) and photosynthesis rate (a, c; Eq. 5), growth rate (b; Eq. 3) and calcification rate (d; Eq. 6). Notes: in panels (a) and (b) red dots are the experiments from the coccolithophore database carried out in optimum growth conditions and grey dots are published field measurements of metabolic rates for a large number of organisms  (Marañón, 2008) ; in panels (c) and (d) symbols denote coccolithophore species (see legend) and all data refer to optimum growth conditions. The dotted lines are the linear regressions through the experimental coccolithophore data obtained in optimum growth conditions and the field data of  Marañón (2008;  see Table  2  for definition of optimum growth conditions).
, 2014; Marañón et al. , 2013). In this context the coccolithophore data set is particularly relevant because it fills in the gap of sizes between 10 0 and 10 3 µm 3 that is underrepresented in the  Marañón (2008)  data set. Furthermore, it confirms that a scaling exponent significantly smaller than -1/4 occurs in laboratory conditions, in addition to field situations, suggesting that cell size is not an important factor in determining the size distribution of coccolithophore populations. Taken together, the near-isometric scaling of growth rate with cell size observed in the ocean by  Marañón (2008)  and in the laboratory  (López-Sandoval et al. , 2014; Marañón et al. , 2013)  suggest that the -3/4 scaling of phytoplankton population abundance with cell size is not uniquely due to an effect of cell size on growth rates.
Environmental controls on cell size and metabolic rates in coccolithophores In this section I investigate how changes in environmental conditions affect cell size and metabolic rates in coccolithophores. The changes we will deal with are produced by the physiological response of a given taxon to environmental change; I will discuss the effects of six environmental variables: pCO 2 , irradiance, temperature, nitrate, phosphate and iron. Next to the optimum group of experiments introduced in Sect. 3, I highlight light-limited, nitrate-limited, phosphate-limited and iron-limited experiments. The set of conditions defining these groups is detailed in Table  2. Most of the data (82 % of database entries) come from cultures of E. huxleyi, the more thoroughly studied coccolithophore; experiments with the other four coccolithophores in the database have essentially tested the effect of pCO 2 conditions on growth, photosynthesis and calcification.
A better picture is obtained if changes in cell volume are plotted against changes in metabolic rates. I have explored the database for sets of experiments where only one experimental condition is changed at a time, so that the change in volume and metabolic rates can be calculated by subtraction and plotted. In this way different sets of experiments can be compared on the same plot (this procedure is explained in detail in Appendix A3). The plots show the changes in metabolic rates and cell size induced by an increase in pCO 2 ; an increase in irradiance starting from light-limited conditions; an increase in temperature; and a decrease in nitrate, phosphate or iron starting from nutrient-replete conditions (Figs. 5  and 6 ). These changes correspond to the evolution of the living conditions that phytoplankton are experiencing (warming, acidification) or are expected to experience (ocean stratification leading to increased irradiance and oligotrophy) in the coming centuries  (Behrenfeld et al.
, 2007). The concomitant decrease in cell size is consistent with the size shifts observed in the experiments where pCO 2 is varied (Fig. 5 ). It should be noted that the coccolithophore database is strongly biased in favour of experiments with the coccolithophore E. huxleyi (82 % of database entries), and more experiments with other species are needed to understand whether the above relations between environment, cell size and metabolic rates can be extended to coccolithophores in general. Furthermore, the experiments included in the coccolithophore data set are designed to quantify the instantaneous (meaning a few generations) response of coccolithophores to changing growth conditions. In longer-term experiments, lasting several hundred generations  (Lohbeck et al. , 2012; Schlüter et al.
I decided to use Eq. (  7 ), rather than the equation of  Montagnes et al. (1994) , because the equation of  Montagnes et al. (1994)  implies a much lower density of carbon per cell (0. 1 pgC µm -3 ) and would result in E. huxleyi spheres larger (up to 12 µm diameter) than those observed in culture and in the field. Similar to E. huxleyi, if the relation between cell volume and carbon quota per cell of  Montagnes et al. (1994; Eq. A13)  is applied to the Coccolithus braarudii POC data, then the resulting coccosphere diameters for most of the coccospheres in the database (20-25 µm) are higher than those reported in Henderiks  (Henderiks, 2008; 18-22 µm; Fig. 2c). Figure  2  shows that the measured coccosphere diameter is always smaller than the coccosphere diameter calculated with the geometric model (Eq.
7). The large majority of coccosphere-size measurements in the database were carried out with Coulter counters (Table  A2 ). It is known that cellsize measurements obtained with the Coulter counter underestimates the real coccosphere size as measured by SEM, possibly because the Coulter counter does not see the coccolith shield  (Oviedo et al. , 2014). Iglesias-Rodriguez et al. (2008)  also report coccosphere-size measurements obtained with Coulter counters that are significantly smaller those obtained with flow cytometry. In fact, their Coulter counter measurements are very similar to the flow cytometer measurements after acidification of the sample, consistent with the idea that the Coulter counter does not see the coccolith shield  (Oviedo et al. , 2014). Similarly, by comparing light microscope measurements with Coulter counter measurements,  van Rijssel and Gieskes (2002)  report that the Coulter counter does not see the coccosphere.
General Bathymetric Chart of the Oceans (GEBCO) has released the GEBCO_2014 grid, a new digital bathymetric model of the world ocean floor merged with land topography from publicly available digital elevation models. GEBCO_2014 has a grid spacing of 30 arc sec and updates the 2010 release (GEBCO_08) by incorporating new versions of regional bathymetric compilations from the International Bathymetric Chart of the Arctic Ocean, the International Bathymetric Chart of the Southern Ocean, the Baltic Sea Bathymetry Database, and data from the European Marine Observation and Data network bathymetry portal, among other data sources. Approximately 33% of ocean grid cells (not area) have been updated in GEBCO_2014 from the previous version, including both new interpolated depth values and added soundings.
It includes recently released regional compilations such as the International Bathymetric Chart of the Southern Ocean (IBCSO)  [Arndt et al. , 2013] , the International Bathymetric Chart of the Arctic Ocean (IBCAO)  [Jakobsson et al. , 2012] , the Baltic Sea Bathymetry Database (BSBD)  [Hell and Öiås, 2014] , and the European Marine Observation and Data Network (EMODnet) 2013 database for European waters  [Schaap and Moussat, 2013; Schmitt and Weatherall, 2014]  (http://www. emodnet-bathymetry. eu). Although GEBCO_2014 covers the globe with a 30 arc sec grid, only about 18% of the grid cells over the oceans are constrained by measured data or preprepared grids which may contain some interpolated values. In previously released GEBCO_08, it was not possible to calculate the level of constraint due to lack of source data information, i.
e. , metadata. GEBCO_2014 is not only improved with respect to source data density compared to previously released versions, but its companion Source Identifier (SID) grid provides metadata that includes contributor source attribution as well as constraint information. Even though the GEBCO bathymetric database has grown substantially over the last decade, the majority of the deep ocean basins as well as the Arctic and Antarctic regions remain sparsely mapped  [Smith, 1993; Wessel and Chandler, 2011]. Earth and Space Science 10. 1002/2015EA000107 Much of the bathymetric data from these regions are older and poorly navigated  [Smith, 1993] , leaving huge areas of the world ocean floor unsounded with modern multibeam technology.
Hall. [Hall, 2002] -also included in GEBCO_08 For this area land data are largely taken from the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) data set  [Danielson and Gesch, 2011]. Over Greenland the approximately 2000 × 2000 m resolution digital elevation model published by  Ekholm [1996]  is used. For Antarctic regions south of 60°S Land and ice shelf regions are based on the Bedmap2 data sets. [Fretwell et al. , 2013]  Earth and Space Science 10. 1002/2015EA000107 Regional compilations (e. g. , IBCSO, IBCAO, BSBD, and EMODnet) cover approximately 25% of the world ocean area in GEBCO_2014. In the sparsely surveyed deep ocean basins, GEBCO_2014 relies heavily on depth values derived using the satellite altimetry method. Figure  2  shows the data sources used in GEBCO_2014.
Regional Seafloor Mapping Contributions Recognizing the importance of local expertise when building a global bathymetric model, GEBCO set up the Sub-Committee on Regional Undersea Mapping (SCRUM) to build a close collaboration with regional mapping efforts to coordinate and encourage the inclusion of their compilations into GEBCO. The GEBCO grid has benefited from this initiative and includes compilations from IBCAO and IBCSO and waters around Europe from EMODnet and BSBD. In addition, a regional grid developed by the Japan Hydrographic Oceanographic Department has been made available through the Japan Oceanographic Data Center (JODC). This substantially improved the portrayal of the northwestern Pacific Ocean region in GEBCO_2014 Earth and Space Science 10. 1002/2015EA000107 compared to previously released versions.
Gridding and Updating Many of the new bathymetric data sets included in this release of the GEBCO grid are from grids provided by project groups collaborating with GEBCO (Table  1 ). They have largely been included in the GEBCO grid using the "remove-restore" procedure  [Hell and Jakobsson, 2011; Smith and Sandwell, 1997]. In Figure  4 , we illustrate the remove-restore procedure in a section of the Mediterranean Sea, in which dense multibeam data from EMODnet have been incorporated into GEBCO_2014. The bottom layer is the GEBCO_08 grid, which here has only widely spaced sounding constraints. The middle layer is a grid interpolated from the difference values obtained by removing GEBCO_08 depths from EMODnet multibeam depths. The GEBCO_08 grid is then restored to the interpolated differences, forming the GEBCO_2014 grid (top layer).
The remove-restore procedure both optimizes the preservation of details of the higher-resolution data while giving a smooth transition with the surrounding base grid. Indeed, fine-scale seafloor details that are only hinted at in GEBCO_08 are resolved in GEBCO_2014. This methodology has been used in the development of the IBCAO and IBCSO gridded data sets. Details of the development of the IBCAO grid (including scripts) can be found in chapter 8. 2. 11 of the IHO-IOC GEBCO Cook  Book [2014]. The spline parameters used are described in the individual release articles for the regional compilations. Data sets for updating the GEBCO grid have also been supplied in the form of individual soundings, e. g. , soundings from Electronic Navigation Charts (ENCs). These data, along with additional survey data in the area, were combined and then gridded using an adjustable tension continuous curvature gridding algorithm "surface" from the Generic Mapping Tools system  [Wessel and Smith, 1998; Smith and Wessel, 1990 ].
Results and Discussion GEBCO_2014 provides a more detailed portrayal of the world ocean floor than GEBCO_08. This is due not only to the updated regional compilations and additional track line data that together comprise approximately 33% of the 30 arc sec grid cells for the entire world ocean area but also to adding a large amount of individual bathymetric data. Bathymetric surveys on a finer spacing than the 30 arc sec grid have also been incorporated using the remove-restore method to enhance fine-scale details. This process will continue as new data become available, enabling incremental updates to the GEBCO grid. However, where the bathymetric grid depends on depths estimated from altimetry, the physical limitations of satellite data collection impact the resolution of gravity anomalies arising from seafloor structures.
0  [Jakobsson et al. , 2012]  (included in GEBCO_2014), over the older algorithm used in IBCAO version 2. 0  [Jakobsson et al. , 2008]  (included in GEBCO_08). The large and widespread differences in the Southern Ocean revealed by the difference grid are due to IBCSO version 1. 0 being the first regional compilation released covering this entire area. While GEBCO_08 relied much more on satellite altimetry in the Southern Ocean, the significantly increased number of sounding data improved the newly modeled bathymetry in IBCSO version 1. 0. In addition, IBCSO did not use satellite altimetry for its southernmost areas due to methodology limitations of satellite altimetry in polar regions. In the remaining areas IBCSO used a newly developed gridding algorithm that adjusts the satellite altimetry to the sounding data.
10. 1002/2015EA000107 This measure can be understood as the percentage of vertical changes reported in GEBCO 2014, which is considered to be the reference. Changes smaller than 0. 5% are practically imperceptible at the scale of the GEBCO compilation. The greatest changes generally occur along the coasts, particularly in bays (e. g. , note the bays along the German coast in Figure  6 ). The calculated change values may look excessive in some areas, exceeding 1000%. However, these change values are interpreted to be related to both the limitation of the altimetry-driven interpolation along the coasts and the incorporation of new soundings or data from existing bathymetric grids in these areas where no soundings at all were available during the compilation of GEBCO_08. Moreover, expressing changes between the two grids with the equation above emphasizes the need of highly accurate bathymetric data in coastal and shallow areas.
Bathymetry Model Resolution The resolution of the bathymetry model is a function of the underlying data density-the coverage of satellite tracks for depth estimates and ship soundings. Multibeam surveys collect depth measurements at very high density and may be designed with overlapping swaths to provide full map coverage. When multibeam data are incorporated into the bathymetry model, their high data density improves the resolution of seafloor details. We illustrate this in Figure  7a , which shows the GEBCO_2014 model over a portion of the southern Mid-Atlantic Ridge. Figure  7b  shows the locations of bathymetric soundings used in the model. We compare Earth and Space Science 10. 1002/2015EA000107 two areas on the ridge-one that has been surveyed by multibeam  [Michael et al. , 1994]  (dashed box) and the other with soundings from only a few ship tracks (solid box).
Summary and Outlook GEBCO_2014 is the latest version of the global digital bathymetric model that is available for free download from the Internet in commonly used formats. This updated version includes the most recent regional bathymetric compilations plus new bathymetric surveys and soundings gathered along transit ship tracks. GEBCO digital bathymetric models have been widely used over the past decade, providing users with current bathymetric information for scientific and educational use. The ongoing success of GEBCO community effort depends on continued collaboration and cooperation with IHO Member States; regional hydrographic commissions; regional mapping groups; and research, educational, and commercial institutions. Future versions of the GEBCO bathymetry model will be improved as more bathymetric data are collected, as existing data are made publicly available, and as regional compilations are updated.
Acknowledgments The GEBCO_2014 grid and associated products may be downloaded from http://www. gebco. net, and the GEBCO Digital Atlas Manager, Pauline Weatherall, may be reached at paw@bodc. ac. uk. Building the GEBCO grid is a collaborative and worldwide effort. All the bathymetrists who have contributed to describe the ocean floor are associated with this publication. The comments of J. J. Becker and an anonymous reviewer improved this manuscript. We thank Walter H. F. Smith for the helpful discussions. The views, opinions, and findings contained in the report are those of the authors and should not be construed as an official National Oceanic and Atmospheric Administration or U. S. Government position, policy, or decision.
In this work we compare the annual component of sea level variations derived from 478 worldwidedistributed tide gauges with the annual component computed from a weekly gridded multi-mission altimeter product. Gridded altimetry data products allow for spatio-temporal analyses that are not possible based on along-track altimetry data. However, a precise validation is necessary in the coastal region before the gridded data can be used. Results of the comparisons show that root-mean-square differences (RMSD) between the two datasets are r2 cm for 76. 4% of the sites. RMSD higher than 4 cm are caused by narrow coastal currents, nearby river outflows or other local phenomena. A methodology is proposed to assess the accuracy of the seasonal component of the gridded altimeter product in regions with a low density net of tide gauges.
Contents lists available at ScienceDirect journal homepage: www. elsevier. com/locate/csr The above-mentioned efforts to improve the quality of the altimetry data in coastal regions have encouraged studies at regional and global scale. In particular, satisfactory results have been found at seasonal scales using different altimetry products. Volkov and Pujol (2012)  showed that in the Nordic seas gridded altimetry and tide gauge data are in good agreement in terms of amplitudes and phases of the seasonal cycle. Gridded altimetry data were also validated at seasonal and interannual scales in the Gulf of Cadiz by  Gómez-Enri et al. (2012). At global scale,  Vinogradov and Ponte (2010)  compared the annual cycle derived from TOPEX/POSEIDON along-track data with the annual cycle derived from 345 coastal tide gauges.