dataesr/scientific-paragraphs-categorization huggingface dataset:
g. average global value). Similarly to our calculation of "dataset selection" error, this can be converted to percentage error by considering the range in these values and dividing them by minimum and maximum values. For the "observational error" we refer to  Chance et al. (2019b) , who provide individual error estimates for each of the iodide observations in the data compilation. Over half (51 %) of the data points have an error of 5 % or less, and a further ∼25% have an uncertainty in the range of 5-10 %. We therefore use a value of 10 % as a conservative estimate of the "observational error".
Outlier identification and removal Our dataset consists of values for ancillary variables and iodide concentration for all of the 1342 measurement locations in the observational dataset (Sect 2). As discussed in Sect 3. 1, we split this dataset into two parts: (i) a training set for use in building and optimising models, and (ii) a withheld set to evaluate the models built. Particular care was taken to ensure the withheld and training datasets were representative of the entire dataset in the way the models built, therefore improving performance and "generalisability" to unseen data (See Sect. 3. 1). We take a Random Forest Regressor (RFR) model built with variables that were intuitively assumed to give a reasonable ability to differentiate the observations (using depth, temperature, and salinity as the independant variables -abbreviated to "RFR(DEPTH+TEMP+SAL)" following Table  1 ).
The "RFR(DEPTH+TEMP+SAL)" model was then used to explore the variation of error in the predictions using the "dataset selection" error approach described in Sect. 3. 2. This builds multiple versions of the same model with different splits of training data and yields a distribution of Root Mean Square Error (RMSE) in the predicted iodide for withheld data as summarised in the final column of Table  2  and shown graphically in Appendix Fig. A1. We define outliers here as values greater than the 3 rd quartile plus 1. 5 times the interquartile range  (Frigge et al. , 1989). Removing these forty nine values categorised as outliers (>309. 5 nM) leads to a vast improvement in the RMSE error in the ensemble prediction from 95. 1 nM to 37. 6 nM (Table  2 ). This is shown graphically in Appendix Fig A1 , with the other subsets of the data explored (Table  2 ).
This demonstrates that the high values are not well enough represented by the dataset to be able to be captured by the RFR approach. The removal of these high values from the dataset can also be justified as the driver for these concentrations is not yet well understood  (Chance et al. , 2014 (Chance et al. , , 2019c;; Cutter et al. , 2018). Removing these outliers reduces RMSE in the prediction with the twenty independent model builds from 48. 2 nM to 2. 3 nM (3 rd quartile -1 st quartile). Once these outliers are excluded, more modest changes in average RMSE are then seen if models are built only using coastal or non-coastal data. Fig. A1  also shows this is seen when removing lower salinity data ('Salinity ≥30 PSU & no outliers'), which is indicative of estuarine water. This highlights the strength in this approach's ability to predict iodide in different biogeochemical regions (i.
e. not just coastal or non-coastal locations). An additional removal of a single dataset of nineteen observations from the Skagerrak strait  (Truesdale et al. , 2003)  was made due to it exerting a disproportionate influence on iodide prediction in high Northern latitudes (>=65 • N), an area that is almost entirely unconstrained by local observations. We note that the Skagerrak is relatively unusual oceanographically, being an estuarine location with high ship traffic, and is considered unlikely to be an analogue for iodine speciation in the Arctic. This is decision is discussed further in Appendix A1 and the predictions made including this dataset are also included in the shared output (Sect. 5). From here, only the 1293 observational points excluding outliers and the data from the Skagerrak strait  (Truesdale et al.
Model Descriptions Unlike many machine learning approaches, the Random Forest Regressor algorithm is interpretable. The decision trees can be visualised to explain the main features driving the splits. Figure  3  shows schematically the whole regression approach taken here. Panel (a) shows single trees, of which 500 are built with the same input variables and then combined into forest (b). Then this forest is combined with the nine other top-performing models (made from different combinations of ancillary variables) to make an ensemble (c). The ten predictions of (c) are then arithmetically averaged into a single prediction, which thus includes the predictions of 5000 trees with 10 different combinations of input variables. In Fig  3a , the colour of a limb or "branch" following a node is given by the variable driving that split within the training dataset.
For Fig. 3b  and 3c it shows the percent of that times that a variable drives that node within the forest. The value of the ancillary variable that sets the split is shown inside the circle (a,b,c). The thickness of the branch scales to the throughput of training dataset samples contained within that split. The trees are shown to a depth of five nodes for aesthetic reasons and due to increased divergence of the trees within a forest the deeper you go. However the trees themselves are unlimited in the depth they can reach. The first and larger splits in the data at decision "nodes" in the models can be simply read, which can provide understanding of the main variables driving the initial and largest splits in the prediction. For all models in the ensemble, the initial split is driven by temperature, with a split occurring at around 21.
1 • C (with a standard deviation of 1. 2 • C). The data is then split by two further nodes from this, a left and right hand split (e. g. Fig. 3b ). If depth or temperature is present as a variable, then they drive the majority of the next splits. If depth is not present as a variable, then either nitrate or mixed layer depth (MLD) is the most common variable to dictate the split in the data at the next node in the tree. Thus a qualitative way of interpreting the initial splits of the dataset would be to say that the model is primarily differentiating between warmer and shallower locations.
Results Here we evaluate the performance of the ensemble prediction against the observational dataset (Sect. 4. 1) and then we explore the predicted global monthly surface concentrations (Sect. 4. 2). The "dataset selection" error, which shows the the influence of the choice of how the dataset is split into training and with data on model prediction, is described in Section 3. 2. Within the 20 member ensemble of different testing/withdrawn choices, the average variation in RMSE was 8. 4 nM (5. 9-11. 02 nM) and in the range of average predicted values was 6. 1 nM (5. 4-6. 6 nM). This translates to a percentage error of 16. 1-29. 5 % on the RMSE and 5. 6-7. 0 % on the average predicted value.
Prediction of iodide at observational locations The "model selection" error, which is the influence of the different independent variables used, is described in Section 3. 2. The difference in the average prediction of the 10 members of the ensemble is 1. 8 nM (with a range of average prediction from 96. 0 to 97. 8 nM) and the range of the difference in model performance is 3. 9 nM (33. 2-37. 2 nM). As a percentage this "model selection" translates to a percentage uncertainty on the the RMSE of 10. 6-11. 9 % and on the average of 1. 8-1. 9 %. The "dataset selection" and "model selection" compares to an error on the observations of ∼10 %. Uncertainty from "dataset selection" has a far greater effect on the prediction error than "model selection". This is can be expected due to the small dataset size.
The combined error in the prediction ("dataset selection"+"model selection" error) is either comparable to (7. 4-8. 9 % in terms of average prediction) or greater (27-41 % in terms of RMSE) than the observational error. From this analysis we have shown that the new ensemble RFR model performs significantly better than those currently in the literature. We now turn to explore the predicted global distribution of sea-surface iodide using our ensemble model.
(2014)  and MacDonald et al. (2014), and the observations are shown in Fig. 7. Far greater structure is seen compared to the two existing parameterisations (Fig. 7 ) due to the multivariate and non-parametric ensemble approach used here. All parameterisations capture the broad observed feature of decreasing iodide from lower to higher latitude. The new predicted values lay between  Chance et al. (2014)  and  MacDonald et al. (2014)  in the tropics, however, within the polar regions, the new prediction is significantly higher than both of the previous parameterisations. The lower concentrations in the predicted values from MacDonald et al. (  2014 ) for most of the global sea-surface is clear. The "dataset error" is found for the 20 models with different training data splits, as described in Sect.
3 to 108. 8 nM. This range in prediction gives a "model selection" error of 6. 45 nM, which equates to 6. 0-6. 3 %. Like with the global uncertainty from "dataset selection", the global value would be expected to be lower than the uncertainty at the specific locations of the observations (Sect. 4. 1) due to the more homogeneous nature of the predicted areas. However, a greater variation is seen from different model predictions than within predictions for the observation locations. This highlights the importance of the different ancillary variables considered here and also therefore the strength gained from the ensemble approach taken here. Within members of the ensemble, variation is modest except for two ensemble members which divergence north of >=65 In addition to the three errors we described above, we also attempt to gain to an understanding of the spatial uncertainty in the ensemble prediction.
Data availability The monthly ensemble mean and standard deviation between ensemble members for the main prediction presented here ("RFR(Ensemble)"), along with the individual ensemble members are archived at the United Kingdom's Centre for Environmental Data Analysis (CEDA) as monthly files in NetCDF-4 format  (Sherwen et al. (2019) ; DOI:https://doi. org/10/gfv5v3). To enable use in atmospheric and oceanic models, we have additionally bi-linearly re-gridded the outputted fields onto common model grids (Appendix Table  A5 ) using the open-source Python xESMF package  (Zhuang, 2018). We recommended use of the standard output provided, but have also provided the predictions made by the model with the Skagerrak dataset  (Truesdale et al. , 2003)  included (which was excluded from the analysis presented here, as discussed further in Appendix Sect A1).
This would result in larger decreases in tropospheric ozone burden than previously suggested  (Sherwen et al. , 2016a). A higher iodide sea-surface concentration would also result in a greater calculated ozone deposition  (Luhar et al. , 2017; Sarwar et al. , 2016). We have calculated the errors in sea-surface iodide concentrations at observational locations due to the "dataset selection" of 16. 1-29. 5 %, and due to "model selection" of 1. 8-1. 9 % (Sect 4. 1 and 4. 2). These error estimates can be compared to an approximated error in the observations of ∼10 %  (Chance et al. , 2019b   The choice of the algorithm used here is subjective and numerous other options are available. The Random Forest Regressor was chosen due to appropriateness for the continuous regression task performed here, its relatively cheap computation cost, and its interpretability.
Considering the greatest uncertainty is driven by the paucity and sparsity of observations, using more complex techniques would not be expected to yield particularly different or drastically better results, considering other tradeoffs. Earth System Science Data Discussions Manuscript under We have developed a new way to build a spatially and temporally resolved dataset from a spatially and temporally sparse input of observations. This has allowed for use of use more of the observations, than traditional approaches, which is particularly important with a paucity of data. This approach has demonstrated a large improvement in skill in terms of capturing observations compared to the existing parameterisations in use. It captures the increasing trend of iodide with latitude seen in the observations, as well as the greater spatial variation seen in the observations.
Hamman, 2017) and Scikit-learn  (Pedregosa et al. , 2011). Spatial re-gridding used the xESMF package  (Zhuang, 2018). Plots presented here were created using the Matplotlib  (Hunter, 2007)  and Seaborn  (Waskom et al. , 2017)  python packages. The decision tree figures (Figs. 3  and A5 ) were made using the TreeSurgeon (Ellis and Sherwen, 2019) package. Right hand y-axis is standard deviation of the prediction for the withheld data (orange). Top ten performing models and the two exiting parameterisations considered here  (Chance et al. , 2014; MacDonald et al. , 2014)       Solid lines give mean values and shaded regions give (±) the average standard deviation. The standard deviation is the monthly standard deviation across a latitude between all ensemble members ("RFR(Ensmeble)") or within a single prediction for existing parameterisations  (Chance et al.
Figure4shows a point-by-point comparison between parameterised and observed iodide for the: entire dataset; the withheld dataset; withheld coastal dataset and withheld non-coastal dataset. Predictions are shown for the ensemble Random Forest Regressor (RFR) approach described here, and for both the Chance et al. (2014)  andMacDonald et al. (2014)  parameterisations. The Root Mean Square Errors (RMSE) of observed and predicted values are given in the Figure4and in Table3.
Acknowledgements. We all thank those who have provided their published and unpublished data to the Chance et al. (2019b) dataset and the  British Oceanographic Data Centre (BODC)  for hosting this, as well as all support staff involved. We gratefully acknowledge those who have worked to compile and make avialible the ancillary datasets used here (Table 1). These include the  World Ocean Atlas (WOA) ,  Ocean Biology Processing Group (OBPG)  at  NASA 's  Goddard Space Flight Center , and the  General Bathymetric Chart of the Oceans (GEBCO)  teams. We thank  Tim Jickells ,  Peter Liss ,  David Stevens , and  Martin Wadley  for their useful conversations on and comments about this work. We gratefully acknowledge funding from  Natural Environment Research Council (NERC)  through grants "Iodide in the ocean: distribution and impact on iodine flux and ozone loss" ( NE/N009983/1 ) and " Big data  for atmospheric chemistry and composition: Understanding the  Science (BACCHUS) " ( NE/N009983/1 ).
Acknowledgments We are grateful to  Dr. Marina Potapova , of the  Academy of Natural Sciences, Philadelphia , who examined type slide of C. arenicola and provided LM images. Thanks are due to  Mr. Kim Govers  and  Miss Juliana Bettig of the Botanical Museum, Berlin  for their help with SEM observations. The first author is grateful to professor  Inger Wallentinus  of the  Department of Biological and Environmental Sciences, Gothenburg University  for identifying the macrophytes. This research received support from the  SYNTHESYS  Project (http://www. synthesys. info/),financed by the  European Community Research Infrastructure Action  under the  FP7 "Capacities" Program.
However, adipogenesis was far lower when compared to VIS low APs (Figures  S3D-S3M ). These results suggest that VIS high APs have the potential to differentiate into adipocytes although less than VIS low APs. We next examined whether signaling pathways known to suppress or promote adipogenesis were altered in VIS high APs. Thus, we investigated the anti-adipogenic Wnt/b-catenin signaling  (Rosen and MacDougald, 2006). However, we found no difference in b-catenin expression between VIS high and VIS low APs (data not shown). Finally, to exclude the possibility that VS high APs were unable to respond to DM, we measured phosphorylation of insulin receptor b (IRb), Akt, and Creb after 24 h of differentiation. We found a significant 31% reduction in IRb tyrosine phosphorylation in VIS high APs compared to VIS low APs  (Figures S3N and S3O).
These data confirmed that VIS high APs not only proliferate faster but negatively influence the adipogenic capacity of both VIS low and SUB APs through the secretion of soluble factor(s). We next sought to identify abundant proteins in CM from 3-days-differentiated VIS high and VIS low APs using proteomics (PXD014672). As depicted in Figures  3A  and S4A , we found that VIS high APs secreted more factors than VIS low APs. Among the most significantly (R2-fold) abundant proteins in CM of VIS low APs were angiotensinogen (Agt), lipocalin 2 (Lcn2), and retinolbinding protein 4 (Rbp4), which we did an ELISA and confirmed its presence in CM of VIS low cells (Figures  S4A  and S4B ). The presence of these proteins in CM of VIS low APs is consistent with their conversion to adipocytes. In contrast, VIS high APs secreted mostly extracellular matrix (ECM) and cell adhesion molecules and some cytokines (Figures  3A  and S4A ).
Histology and Morphometry Epididymal (eWAT) depot was fixed in 4% paraformaldehyde for 48 h, embedded in paraffin, cut into 5-mm sections, and stained with Hematoxylin and Eosin (H&E). The adipocyte diameter was determined as previously described  (Wheatcroft et al. , 2007). The total number of adipocytes was measured using the CellSens Software. The images were acquired with an XM10 Olympus fluorescent camera.
Slides were then incubated with blocking solution (1% bovine serum albumin, 1% goat serum, 22. 52 mg/ml glycine in PBS with 1% Tween-20. Slides were then incubated with either anti-GFP, anti-Mac1 or anti-Pdgfra antibody at room temperature for 1 h then at 4 C overnight. Wells or slides were washed three times in PBS and then mounted with ProLong Gold Antifade Mountant with DAPI. Images were acquired using an inverted IX-71 Olympus epi-fluorescence microscope equipped with a digital XM-10 camera and Cell Sense software package (Olympus, Valley, PA).
MS1 scans were acquired in the Orbitrap mass analyzer at a resolution of 60000. MS1 ions were fragmented by CID fragmentation with an activation time of 10 ms and a normalized collision energy of 35. Dynamic Exclusion was enabled for 60 s intervals to avoid multiple fragmentations of parent ions. The raw data files were analyzed using MaxQuant  (Li and Picard, 2010)  and searched against the Uniprot mouse database. The database search was carried out using the in-built Andromeda search engine and label-free quantitation was carried out using the MaxLFQ module  (Shen et al. , 2015). A mass tolerance of 20 ppm for precursor ions and 0. 5 Da for fragmented ions was used during the search. The peptides were searched for the static modification of carbamidomethylation on cysteine and the variable modification of methionine oxidation and protein N-terminal acetylation.
DATA AND CODE AVAILABILITY Proteomics data reported in this paper are deposited in ProteomeXchange data base with the accession number PXD014672. C o l 1 a 1 C o l 1 a 2 C o l 3 a 1 C o l 4 a 1 C o l 4 a 2 C o l 5 a 1 C o l 5 a 2 C o l 1 5 a 1 F b l n 1 F b l n 2 F n b 1 F l n a F n 1 C d 9 T g f b i L a m b 1 T h b s 1 E m i l i n 1 P p a r g.
ACKNOWLEDGMENTS We thank  Dr. Gisela Geoghegan  and  Dr. Claudio Villanueva  for their technical assistance with gene expression analysis. We thank  Dr. Hansjorg Schwertz  for his technical assistance with protein labeling. We thank  Dr. Peter D. Pioli  for his technical assistance with bone marrow transplantation. We thank  Dr. Scott Summers  for his valuable comments on the manuscript. We would like to thank  Mr. James Marvin  at the  University of Utah flow cytometry core facility  for assistance with cell sorting. We thank  Mrs. Diana Lim  for her assistance making the figures and the graphical abstract. This work was supported by the  NIH   R01DK098646  (S. B. ) and  R01DK100826  (S. B. ) and the  American Heart Association (AHA)   16GRNT30990018  (S. B. ) and  15POST25360014  (K. M. P.
Tagged individuals were marked with numbered steel rings on the other leg. A blood sample for sex determination was taken from the wing vein and stored on FTA cards. Burrows were revisited and devices retrieved during incubation in the following season (Table  1 ). Because several loggers stopped recording several months before device recovery, the final sample sizes for year-round tracks, which were used in this study, were smaller than for recovered data sets (Table  1 ). The GLS recorded data from a single pre-breeding period for each individual. Several pairs were included (Falklands 2010-11: 8 pairs, 2013: 1 pair, Kerguelen: 2 pairs), but as their movement patterns were independent (Additional file 1: Figure  S1 ), they were included as independent data. , The number of pairs with successful data retrieval was not great enough, especially for Kerguelen birds, to analyse data at the pair level.
Data processing Geolocators provide two positions per day based on light levels, with an accuracy of approximately 186 ± 114 km  [19]. Light data were analysed using the BAS-Trak software suite (British Antarctic Survey, Cambridge, UK). TransEdit was used to check for integrity of light curves and to determine dawn and dusk times, and Locator to estimate the latitude from day length and longitude from the time of local mid-day relative to Greenwich Mean Time. We assumed a sun elevation angle of -3. 5°, based on known positions obtained during pre-and post-deployment calibration of the loggers at the colony. All estimated locations were examined visually in a geographical information system (GIS) and any unrealistic positions in the year-round trackseither associated with interference to light curves at dawn or dusk, or in proximity to equinoxes when latitudes are unreliable -were excluded from further analyses.
However, this did not affect the honeymoon or incubation periods, when data were complete. Trips to sea were distinguished from periods in the burrow by examining the light and immersion data. Following  [8] , the occurrence of complete daytime darkness in the logger trace allowed identification of days spent in the colony, whilst sustained periods of night-time dryness in the immersion data (> 2 h) allowed identification of visits to the burrows during the night. The pre-laying exodus was an obvious phase lasting 14-41 days when the bird was at sea, which preceded the first incubation shift. It was also possible to determine the day of first arrival in the colony, the period from first arrival to the start of the pre-laying exodus (hereafter, the "pre-exodus phase"), and the total number and proportion of days spent in the burrow during this phase.
Statistical data analyses Statistical analyses were conducted using SPSS 11. 0. We tested for normality using Kolmogorov-Smirnov tests and by checking plots of the data. General linear models (GLMs) based on Type III sum of squares were used to test for differences in the timing and distribution of each parameters among years and sexes. We carried out a separate GLM for each timing and distribution parameter, with sex and year included as categorical independent variables ("factor"). Initially, we included the interaction between the two factors, but this was removed if non-significant (e. g. [32] ). As a measure of effect sizes we used partial Eta-Square values (η 2 ; i. e. the proportion of the effect + error variance that is attributable to the effect) in case of variables and covariates tested with a GLM.