dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , the peak often occurs near 75 • N in the 1984-1993 datasets in Fig. 11 ). Nevertheless, Fig. 11  displays qualitative trends: (i) the seasonal progression of the hemispheric asymmetry, warming in the spring and cooling in the autumn; (ii) the peak emission from the summertime poles as the North/South polar stratospheric vortices (NPSV, SPSV) form  (Fletcher et al. , 2018b) ; and (iii) the change in low-latitude temperature contrasts associated with Saturn's equatorial stratospheric oscillation  (Orton et al. , 2008). We shall return to each of these points below. Given the proposed semi-annual nature of the equatorial oscillation  (Orton et al. , 2008; Fouchet et al. , 2008) , our first-order assumption would be that Saturn should appear the same from one Saturnian year to the next. Thus temperatures measured by Cassini/CIRS near the end of the mission (  = 93 • , September 2017) should provide a good approximation to the first 2D thermal images of Saturn observed one Saturnian year earlier (March 1989,   = 103 • ,  Gezari et al.
Comparison with radiative models With the exception of the variability associated with the equatorial oscillation, and the strong emission from the northern storm 'beacon' in 2011-2014, the ∼37-year time series in Fig. 11  suggests a slow seasonal reversal in hemispheric contrasts associated with radiative heating and cooling. In this section we compare the observations with predictions based on the radiative climate models of  Guerlet et al. (2014)  and  Hue et al. (2016). SPICE software tools provided by the Navigation and Ancillary Information Facility (NAIF,  Acton, 1996) , accessed via 'SpiceyPy'  (Annex et al. , 2020) , were used to calculate Saturn's ephemerides at all oppositions (as viewed from Earth) between 1980 and 2030. For each opposition, visible latitudes, longitudes, illumination and viewing angles were projected onto the sky using the 0.
(2016)  model, and although these are closer to the observations, the absence of circulation makes the comparison challenging. Furthermore, there is a subtle shift in the timing of the maximum brightness by approximately 1 year, closer to solstice for the model of  Hue et al. (2016). Neither the 1985-95 nor the 2015-2020 datasets have sufficient temporal sampling to reliably distinguish between these models. We return to the question of variability in the 2015-2022 observations in Section 5. Finally, we note that neither model does a good job at reproducing the tropospheric brightness. Both predict an equatorial maximum temperature following the annually-averaged insolation, whereas in fact the observed temperatures in Figs. 1 and 2 are more uniform, albeit with the belt/zone variability superimposed on top.
Although systematic differences between observations and models persist, the models successfully reproduce the timing of polar enhancements in temperatures in each year, lagging behind the solstices by a phase difference that increases with pressure. This good correspondence suggests that Saturn's thermal field is largely repeatable from year to year. The first occurrence of the NPSV in  1984 -1985 and 2014 , and SPSV in 2000 , are consistent with both models, confirming that this is primarily a radiative heating effect, albeit with a sharp boundary to the polar vortices introduced by dynamics. Furthermore, the tropospheric polar warming observed at 17-20 μm is also successfully reproduced by the models. However, the temporal sampling of our existing dataset is insufficient to distinguish the subtle differences between models that hold hydrocarbons fixed  (Guerlet et al.
Acknowledgements Blake, Fletcher, Melin,  Roman , Bardet and Antuñano were supported by a  European Research Council   Consolidator Grant  (under the European Union's  Horizon 2020 research and innovation programme , grant agreement No  723890 ) at the  University of Leicester. Some of this research was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration  ( 80NM0018D0004 ). This research used the  ALICE High Performance Computing Facility at the University of Leicester. We wish to thank the many observers and telescope operators who work long nights to provide the community with the data needed to advance planetary science, including (but not limited to)  Dan Gezari ,  Thierry Fouchet , and  Padma Yanamandra-Fisher.
We thank  Sandrine Guerlet  and  Vincent Hue  for making their radiative model predictions available to the community. We thank student interns  Charlotte Mason  and  Zheyu Li  ( Oxford University ), supported by  Caltech 's  Summer Undergraduate Research Fellowship program  at JPL; and  Shana Mastoon  ( Pasadena City College ) supported by  JPL 's  Student Independent Research Internship program. Maël Es-Sayeh  was supported by the  Observatoire de Paris  for an internship at Leicester. Each of these students contributed to preliminary reductions of the datasets presented in this paper. Finally, we thank three anonymous reviewers for their careful assessment of this manuscript. The NEMESIS radiative transfer and spectral retrieval tool (Irwin et al. , 2008) is available via https://github.
com/nemesiscode/radtrancode. The Cassini/CIRS seasonal record of retrieved temperature and hydrocarbons is available here: https://github. com/leighfletcher/CassiniCIRS. This study was based on observations collected at the European Organisation for Astronomical Research in the Southern Hemisphere; on data collected at the  Subaru Telescope , which is operated by the  National Astronomical Observatory of Japan ; and on data collected from the  Infrared Telescope Facility , which is operated by the  University of Hawaii  under contract  80HQTR19D0030  with the  National Aeronautics and Space Administration. Programme numbers are listed, where available, in Table A. 1. The authors wish to recognize and acknowledge the very significant cultural role and reverence that the summit of Maunakea has always had within the indigenous Hawaiian community.
Protist plankton are major members of open-water marine food webs. Traditionally divided between phototrophic phytoplankton and phagotrophic zooplankton, recent research shows many actually combine phototrophy and phagotrophy in the one cell; these protists are the "mixoplankton. " Under the mixoplankton paradigm, "phytoplankton" are incapable of phagotrophy (diatoms being exemplars), while "zooplankton" are incapable of phototrophy. This revision restructures marine food webs, from regional to global levels. Here, we present the first comprehensive database of marine mixoplankton, bringing together extant knowledge of the identity, allometry, physiology, and trophic interactivity of these organisms. This mixoplankton database (MDB) will aid researchers that confront difficulties in characterizing life traits of protist plankton, and it will benefit modelers needing to better appreciate ecology of these organisms with their complex functional and allometric predator-prey interactions.
The MDB also identifies knowledge gaps, including the need to better understand, for different mixoplankton functional types, sources of nutrition (use of nitrate, prey types, and nutritional states), and to obtain vital rates (e. g. growth, photosynthesis, ingestion, factors affecting photo' vs. phago' -trophy). It is now possible to revisit and re-classify protistan "phytoplankton" and "zooplankton" in extant databases of plankton life forms so as to clarify their roles in marine ecosystems.
g. cyanobacteria, diatoms) is quite different; their growth does not directly lead to the death of other organisms, while their consumption of dissolved organics brings them into competition with bacteria and other osmotrophs. Photo-phago-trophy has traditionally been considered to be of relatively minor importance in ecology, although reports of organisms capable of such activity date from the early 20th century (e. g. Pringsheim, 1958; Schiller, 1933). As research progressed, it was also noted that the forms of phototrophy and phagotrophy vary between organisms  (Stoecker et al. , 2009). Now, even species traditionally assumed as exemplar "phytoplankton" (e. g. Emiliania huxleyi,  Avrahami & Frada, 2020 ; Phaeocystis globosa,  Koppelle et al. , 2022)  are recorded as capable of phagotrophy, while over a third of species traditionally labeled as | 3 of 25 THE MIXOPLANKTON DATABASE "microzooplankton" have been found to be capable of engaging in acquired phototrophy (e.
The need for the Mixoplankton Database (MDB) The recognition that oceanic primary producers cannot correctly be analogized as "miniature plants" and their primary consumers as "miniature animals", but includes the mixoplankton, has led to a paradigm shift in the understanding of marine ecology  (Flynn et al. , 2019; Glibert & Mitra, 2022; Mitra et al. , 2016). While there are various databases which contain members from the microbial plankton community, none of these consider facets of protist plankton such as their ecophysiology and/or trophic interactions (including predator-prey dynamics with protists as prey or predators). For example: AlgaeBase focusses on microalgae and thus does not consider protists with acquired phototrophy; IOC-UNESCO HAB database focuses on harmful algal bloom (HAB) species but not on ecosystem disruptive bloom (EDAB;  Sunda et al.
, 2006)  species (such as green Noctiluca); the WoRMs database attributes, at best, functional type characteristics to species but like the others mentioned above provides none of the trophic linkages required to support mixoplankton science. This work explicitly concerns "mixoplankton," rather than mixotrophic plankton in general (see above; Figure  1A ), and presents the first comprehensive database for mixoplankton (MDB;  Mitra et al. , 2023). The MDB is required for marine research because of the increasing understanding of the importance of mixoplankton. This is especially to clarify which protist plankton species are where and what they are doing. In moving from a paradigm of phytoplankton + zooplankton, to phytoplankton + mixoplankton + zooplankton  (Glibert & Mitra, 2022)  we have not just added a new functional group, but we have to redefine the original groups and realign their life-form designations that form the core underpinning of marine science.
Datasets used to support modeling efforts will also need to be reappraised in this context, and the models themselves will require significant attention to reflect the biogeochemical and ecological consequences of including "mixoplankton"  (Ghyoot et al. , 2017; Leles et al. , 2018 Leles et al. , , 2021)). To support such developments, the MDB brings together not just a list of species which have been evidenced as mixoplankton, but also collates information on the sizes and types of the mixoplankton species as well as the types of and sizes of their prey, including (as applicable) similar data about organisms from whence phototrophy is acquired. This database, also for the first time, brings together taxonomic and genetic data on mixoplankton species. The MDB, therefore, provides a platform for future marine plankton research and applications that depend on an understanding of protist plankton and the microbial food web.
Identification of mixoplankton species The genus and species designation of an organism remains the most accurate and widely used identifier, and most reports referencing mixoplankton (and plankton protists in general) identify them that way. Accordingly, for the MDB, data were compiled according to species name as the primary feature. Assembly commenced by building from our previously published datasets, as used for biogeography papers on NCM  (Leles et al. , 2017)  and CM  (Faure et al. , 2019; Leles et al. , 2019). The original definitions of different mixoplankton types by  Mitra et al. (2016) , modified as per  Flynn et al. (2019)  to introduce the term "mixoplankton" in order to avoid the inherent ambiguity of using "mixotroph," were used to differentiate between mixoplankton and strict phototrophs or strict phagotrophs (as per Figure  1A ).
Only species with clear evidence of mixoplanktonic activity (i. e. with documented phototrophy and phagotrophy) were included in the database. To ensure that we included all currently known mixoplankton species within the MDB, we exploited the expert knowledge of the authors with extensive literature searches in the electronic databases-ISI Web of Science and Elsevier over 3 years starting from June 2019. Our literature reviews were conducted in English, German, and French. Evidence of mixoplanktonic activity was traced to the original source, reviewing articles published over a 135-year time span (the earliest being, Haeckel, 1887). Additional mixoplankton identifications have been added as new reports emerged during our analysis, with the most recent additions made in October 2022. A recent dataset, where trophic modes based on  Mitra et al.
(2016)  were assigned to protist plankton species  (Schneider et al. , 2020) , was also interrogated. Schneider et al. (2020)  assumed all species within each genus as listed in our original datasets (i. e. Faure et al. , 2019; Leles et al. , 2017 Leles et al. , , 2019) )  to be mixoplankton irrespective of whether, or not, there was evidence of photo-phagotrophy for individual species. On inspection, some of these assumptions do not appear to be based on published evidence and we found some errors in identification of mixoplankton species within this dataset. Because some of the errors in mixoplankton identification by  Schneider et al. (2020)  appear to have been brought across into the World Register of Marine Species (WoRMS) database, we have not referred to the WoRMS database to verify mixoplankton status of any species; as noted above, we relied solely on primary sources for verification.
The taxonomic grouping of all mixoplankton species within our database has been made in accordance with  Adl et al. (2019). The WoRMS database (https:// www. marin espec ies. org/index. php) was used to provide AphiaID for most species. In instances where a species was not listed in the WoRMS database, we used AlgaeBase (http://www. algae base. org/) to confirm the validity of the species name. Various species have undergone name changes over the years; we provide synonyms, basonyms, and allied information for widely studied species only. For example, Alexandrium pacificum has, in previous publications, been confused with other species from the Alexandrium tamarense/catenella/fundyense species complex. Expert knowledge of our team coupled with AlgaeBase was used to resolve synonyms and basonyms of some species.
The recent proliferation of large-scale environmental DNA sequencing projects in marine ecology offers the opportunity to detect the presence of specific organisms in hundreds of samples of the global ocean  (Santoferrara et al. , 2020; Vernette et al. , 2021). To facilitate the detection of mixoplankton species in such datasets, the MDB compiles all marker genes of mixoplankton species available in the Protist Ribosomal Reference database (PR 2 ,  Guillou et al. , 2013). The list of mixoplankton taxa included in the MDB was used to retrieve the corresponding reference marker sequences in the PR 2 database by use of the PR 2 R package (code available at https:// github. com/MixoH UB/MixoMaps). For each taxon, the MDB gives access to all associated PR 2 and GenBank accession numbers, along with the type of marker (18 S rRNA gene from nucleus or 16S rRNA gene from plastids) and references the publications that generated these sequences.
Those with a "benthic" life form include species with either a partbenthic stage or those that could be primarily benthic but become planktonic (and thence mixoplanktonic) through suspension in the water column. The "parasitic" life form includes species which can become parasitic or can become mixoplanktonic through suspension in the water body. Within the category of life-form traits, we also identify which mixoplankton are documented as HAB species; this was done primarily by interrogating the database on HAB species curated by IOC-UNESCO (https://marin espec ies. org/hab/). Most of these species produce secondary metabolites recognized as toxins  (Hallegraeff et al. , 2021; Reguera et al. , 2012). Some other mixoplankton that are not recorded as "HAB" species in the IOC-UNESCO are harmful to ecology (EDAB species), notably the eSNCM green Noctiluca scintillans; these are also tagged as "HAB" within the database.
Where possible, the numeric cell size (or its range) for each species was documented; these data were obtained from published literature or estimated by the co-authors from published photographs in the Radiolaria database (https://radio laria. org/index. php) and Galerie de l'Observatoire Océanologique de Villefranche-sur-Mer (http://galle ry. obs-vlfr. fr/galle ry2/main. php). In some instances, cell size was measured by co-authors of this work using plankton cultures to which they have access (e. g. data for Noctiluca scintillans). Further, to aid ecological studies (and in the absence of a numeric cell size value), species were categorized according to the traditional standard plankton size categories, namely as-pico: 0. 2-2 μm; nano: 2-20 μm; micro: 20-200 μm; meso: 200 μm-20 mm; macro: 20 mm-2 cm.
For those species capable of forming colonies, the colony size is described both by explicit dimensions (where available) and size categories. The capability of mixoplankton to engage in diverse resource acquisition strategies is a critical trait. A major inorganic nutrient driving primary production in marine systems is nitrate (NO 3 -); here we have recorded the ability of mixoplankton species to use nitrate as a nitrogen source. Also in this resource acquisition category, the database includes each mixoplankton species and their mode of feeding. Colloquially termed "phagotrophy," actual feeding methods involve some combination of raptorial feeding, capture using filters or traps, engulfment of prey, prey lysis (with engulfment or osmotrophy of the remnants), and ingestion using a feeding tube; see Introduction.
Identifying sources of prey and acquired phototrophy Data for the size and taxonomic group of prey ingested by each mixoplankton species are provided within the database. The prey size data include, where available, the size range of prey tested for each mixoplankton species. Additional information about prey sizes is provided for the different ontogenetic stages of the eSNCM Foraminifera within the MDB. As documented for each mixoplankton species, the prey is also classified according to the traditional size categories used in marine plankton science (including also femto, 0. 02-0. 2 μm, for viruses). GNCM and pSNCM acquire phototrophic capability through sequestration of plastids from photosynthetic prokaryotes or eukaryotes, while acquired phototrophy in eSNCM is through endosymbiosis. We provide data on the prey sources for acquired phototrophy for those three functional types, including taxonomic grouping and size of the source organisms.
Global occurrence Data for the global distribution of the mixoplankton species within the database were acquired through interrogation of the Ocean Biogeographic Information System database (OBIS; http://www. iobis. org/) with the most recent data extracted on October 26, 2022. Species names were matched against those in the WoRMS database, which is utilized by OBIS for taxonomic quality control. Geographical coordinates corresponding to the locations where each mixoplankton species was recorded were obtained. Records with possible spatial errors, such as data points located inland, were excluded from the analysis. Geo-referenced occurrence data were retrieved from OBIS using the R package "robis"  (Provoost & Bosch, 2021). Global distribution maps were then generated based on mixoplankton functional type (MFT) and size class across the different Longhurst biogeographic provinces  (Longhurst, 2007).
Grids corresponding to Longhurst's provinces used in the maps were obtained from http:// www. marin eregi ons. org/. At least one record was necessary to assume that mixoplankton occurred in any province. The code used to convert OBIS occurrence data to counts and allocated to Longhurst provinces, and also to construct the biogeography maps, is publicly accessible on GitHub (https://github. com/MixoH UB/MixoMaps); this code can be applied to map the distribution of any species present in the OBIS database by Longhurst province. Marker gene records were retrieved for exemplar mixoplankton taxa through metaPR 2 (metabarcoding Protist Ribosomal Reference database) and the corresponding R package  (Vaulot et al. , 2022). At the time of the last retrieval made (October 28, 2022) metaPR 2 included 4000 samples from all oceans and depths (surface, euphotic, mesopelagic, bathypelagic, under-ice and bottom), spanning six size fractions (from pico-to meso-).
Relationship between MFT and geographic distribution In order to identify exemplar species within each MFT, we conducted a frequency analysis to determine the most frequently (commonly) recorded species in the OBIS database within each MFT, as well as for each size class within each MFT. We also determined the percentage of occurrence of each mixoplankton species across the Longhurst provinces (% LP) using the following equation through which we identified those provinces which had at least 10 independent observations:.
OBIS records versus metaPR 2 records The MDB offers a unique opportunity to compare the global distribution of different mixoplankton species between the OBIS and the metaPR 2 databases. Here we present a study case where we compare the distribution of selected species across the global ocean. Our goal was to evaluate whether the distributions of species differ when looking at OBIS and metaPR 2 databases to describe the limitations and the strengths associated with these databases. We performed a Non-metric Multi-Dimensional Scaling (NMDS) analysis (Table  S1 ). Our matrix contained the number of records for each species within each biogeographic province from both databases. The NMDS technique ordinates the species based on their dissimilarities so that species with similar distributions will be positioned closer to each other relative to other species.
The distance matrix used in the NMDS analysis was calculated using the Jaccard distance after transforming both datasets to presence/ absence data; this is because counts between metaPR 2 and OBIS are not comparable. Ordination was performed using the "metaMDS" function in the "vegan" package in R. We also generated individual global maps for selected species based on the geo-referenced data to illustrate the similarities and dissimilarities between the OBIS and the metaPR 2 sampling effort and coverage.
Quality control The authors, collectively, bring a wide range of skillstaxonomy, genetics, field, laboratory, ecology, modeling-to control the quality of the MDB. As noted above, all data incorporated within the database were curated from original sources. The MDB will be reviewed periodically (at least every 2 years) and updated as science advances; this approach is similar to that employed for updating the classification, nomenclature, and diversity of eukaryotes  (Adl et al. , 2019).