dataesr/scientific-paragraphs-categorization huggingface dataset:
g. Hickox et al. 2009 ) miss obscured and weaker sources. In this paper we classify a sample of RL AGN as HERGs and LERGs on the basis of their broad-band spectral energy distributions (SEDs), and study the RL fractions, radio luminosity functions and colour and mass dependencies for the two classes of RL AGN at intermediate redshifts of 0. 5 ≤ z < 2. Preliminary results were presented in  Williams et al. (2015). This paper is structured as follows: the LOFAR 150-MHz radio data is described in Section 2 and the multi-wavelength datasets and catalogues we use are described in Section 3. In Section 4 we use SED fitting to determine photometric redshifts and galaxy parameters for the sample of optical galaxies. Section 5 describes our method for identifying optical counterparts to the LOFAR radio sources.
B3  for sources with poor fits (quantified by likelihood values -20). We compare the stellar masses and SFRs returned by AG-NFITTER to those we have derived using FAST (see Section 4. 2). This comparison is shown in Fig. 7. We do not use SFRs in the subsequent analysis, but do use the fitted starburst components from AGNFITTER in classifying radio sources as star-forming. While the two codes are used to fit the same data (with the exception that the longest wavelength MIR and FIR data is included for the AG-NFITTER fits), the fitting methods and templates, specifically the inclusion of the AGN components in AGNFITTER, used are inde- pendent. AGNFITTER returns SFRs measured both from the stellar templates in the optical-UV, like FAST, as well as in the IR. In the comparison here, we use the AGNFITTER SFRs inferred from the total IR luminosities, because these are related to the total SB IR luminosity used in the following section to classify star-forming galaxies.
ACKNOWLEDGEMENTS WLW and MJH acknowledge support from the  UK Science and Technology Facilities Council  [ ST/M001008/1 ]. HJR and KJD gratefully acknowledge support from the  European Research Council  under the European Union's  Seventh Framework Programme  (FP/2007-2013) /  ERC  Advanced Grant  NEW-CLUSTERS-321271. PNB is grateful for support from the  UK STFC  via grant  ST/M001229/1. GJW gratefully acknowledges support from The  Leverhulme Trust. MJJ acknowledges support from  UK Science and Technology Facilities Council  and the South.
To clarify the drift character of oxygen optodes, we collected the calibration data of 14 Aanderaa optodes and one Sea-Bird optode that were calibrated at least three times either at the BCCR Bergen/CSIRO-O&A Hobart (12 Aanderaa optodes with 113 calibrations), GEOMAR Kiel (4 Aanderaa optodes with 15 calibrations and 1 Sea-Bird optode with 2 calibrations), or the manufacturer facilities (1 Sea-Bird optode with 1 factory calibration). The BCCR Bergen and CSIRO-O&A Hobart setups are identical. They use a jacketed glass reactor vessel such that a circulating bath can control temperature, and N 2 and O 2 gases together with mass flow controllers to control pO 2. The GEOMAR Kiel setup uses an electrochemical generation of O 2 to control O 2 levels  (Bittig et al. , 2012). All calibrations were performed in freshwater.
Optodes were stored at room temperature, some with and some without wetted sensing foil. Six of the CSIRO-O&A optodes had been "burnt-in" (see details in  Tengberg and Hovdenes, 2014)  with 5 million samples before the first calibration, whereas all other optodes did not receive a similar pre-conditioning. Optode 4330 1082 (model number serial number) received a burn-in with 1. 2 million samples between its second and third calibration, whereas no record of a dedicated burn-in is available for optode 4330 0564. Between calibrations, optodes were deployed on profiling CTDs, surface underway systems in polar or tropical conditions, or kept in the laboratory at all times, i. e. , the sensors were exposed to all kinds of treatments and conditions. A subset of the calibration data for optode 3835 1451 is shown in Figure  6  (a larger subset with all sensors is given in Figure  S4 ).
What Accuracy Can Be Obtained Numerous Aanderaa optodes have been deployed or used with only a factory batch calibration, in particular before the manufacturer introduced its factory multi-point calibration option in mid-2012. While it would be preferable to individually recalibrate each of these optodes, this is not always possible or practical, e. g. , because the sensor is no longer accessible. To assess the quality of O 2 data obtained with such batch calibrated optodes, we compared calibration data from several multi-point laboratory calibrations with the batch calibration procedure. Similarly, (repeated) multi-point calibrations require some effort and may not be possible due to time, logistical or other constraints. We therefore looked at the accuracy of simpler recalibration approaches of multi-point calibrated optodes as well.
During Storage: Timely Reference or (Re-)Calibration The aim for every optode deployment should be to acquire sufficient information shortly before deployment or after recovery to enable a proper drift correction, i. e. , to provide data with at least two (clusters of) reference points. Reference data can originate from any source (e. g. , from a concurrent and ideally co-located Winkler-based CTD-O 2 profile at deployment, hydrographic databases, in-situ in-air measurements, etc. ) since the kind of correction is independent of how the reference data were obtained. Even better but more demanding would be a timely, full multi-point recalibration of the optode to cover the entire O 2 -T-response and to avoid having to use assumptions on the drift character. If it is impossible to obtain any insitu data (e.
Therefore, an individual multi-point calibration for every optode is advisable to allow high-quality O 2 data. In addition, the above Uchida et al. (  2008 )-type mathematical model F (Equation  26 ) has the advantage of a better interpolation and in particular extrapolation behavior with respect to the scattered calibration data compared to the high-order polynomial mathematical models F 3830 and F 4330 (Equation  25 ). However, the mathematical model F is always independent of the calibration or reference data itself. Matlab code to refit calibration coefficients after Equations (26, 31) from Aanderaa batch calibration data sheets is provided by  Bittig (2018).
(2010)  following pO 2,adj = 1+c 4 •ϑ c 5 +c 6 •ϕ adj +c 7 •ϕ 2 adj -1 c 1 + c 2 • ϑ + c 3 • ϑ 2. ( 31 ) that in general yields lower root-mean-squared-errors to calibration data than the original Uchida et al. (  2008 ) model (Equation  26 ), its Sea-Bird modification (Equation  24 ), or the McNeil and D'Asaro (2014) model. Again, the kind of mathematical model F is independent of how calibration data were obtained and any kind of mathematical model can be applied to calibration data of arbitrary origin. Matlab code to refit Aanderaa batch calibration data (as well as Aanderaa and Sea-Bird multi-point factory calibrations for comparison) according to Equation (  31 ) is provided by  Bittig (2018). Comparison of Equations (14, 15, and 29)  gives pO 2 = pO 2,adj • 1 + f (ϑ) • P • exp V L m (O 2 ) • P R • (ϑ + 273.
SUMMARY Driven by the growing interest in the marine oxygen cycle and motivated by the prospects of a global Biogeochemical Argo program (http://biogeochemical-argo. org), the oceanographic community has invested a great deal of effort into characterizing and understanding the promising oxygen optode technology. Through thorough laboratory and field studies on the one hand and careful analyses and theoretical considerations of the data on the other hand, a consistent picture of the properties, limitations and best practices has emerged for the most prominent commercially available oceanographic optodebased oxygen sensors. We have tried to put this here together in a comprehensive but still concise way. The main findings (following the structure of the manuscript) can be summarized as follows: • Oxygen optodes are based on the principle of dynamic luminescence quenching the theory of which is well understood.
FUNDING The authors gratefully acknowledge financial support by the following projects and grants: HB, AK, and JH by the projects  AtlantOS (EU Horizon 2020 research and innovation program , grant agreement no. 2014-633211 ), E- AIMS  ( EU FP7  project  312642 ), as well as  O 2 -Floats  ( KO 1717/3-1 ) and the SFB754 of the  German Science Foundation (DFG) , CN and EvO by the  Integrated Marine Observing System (IMOS) , supported by the  Australian Government  through the  National Collaborative Research Infrastructure Strategy, JP  and KJ by the  David and Lucile Packard Foundation  as well as by  U. S. NSF's Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM)  project under the  NSF  Award  PLR-1425989  with additional support from  NOAA  and  NASA , and  BY  and  SE by U.
ACKNOWLEDGMENTS We would like to acknowledge  Kelly Brown  ( CSIRO-O&A, Hobart ) and  Tobias Hahn  ( GEOMAR, Kiel ) for sensor calibrations, the AADI support for the provision of optode calibration sheets, and  Seth M. Bushinsky  ( Princeton University, Princeton, NJ, USA ) for helpful comments on the manuscript. This work would not have been possible without the  SCOR Working Group 142 "Quality Control Procedures for Oxygen and Other Biogeochemical Sensors on Floats and Gliders. "Argo data were collected and made freely available by the  International Argo Program  and the national programs that contribute to it. The Argo Program is part of the Global Ocean Observing System. We also thank all the people involved in the construction, calibration, and deployment of the profiling floats, moorings, and sensors and the crews of the ships that carried them to sea.
SRTM DEM The SRTM acquired elevation data between 11 and 22 February 2000 using an interferometric aperture radar sensor (SAR)  (Farr et al. , 2007). We used the SRTM DEM processed by NASA from the raw C-band radar signal at 1 arcsecond resolution (about 30 m). Due to radar layover or loss of coherence over vegetation and steep terrain, the SRTM DEM may contain data voids that are sometimes filled by interpolation or using an external DEM. For the purposes of DEM comparison, we use the non-void-filled version in which data voids cover 13% of the total NPI glaciated area. By specific elevation bands the percentage of data voids is 3% below 1,000 m a. s. l (total area 1,163 km 2 ), 22% between 1,000 and 2,000 m a. s. l. (2,323 km 2 ) and 13% above 2,000 m a. s. l. (370 km 2 ). A second, void-filled SRTM DEM (version 4, 3 arcsec resolution) produced by the CGIAR-CSI (http://srtm.
Glacier Outlines, Water Bodies, and Ice Divides Initial outlines for glacier limits and water bodies over the NPI area were obtained from online datasets. The Global Land Ice Measurements from Space (GLIMS) database and the Randolph Glacier Inventory version 5. 0 (RGI 5. 0) offer access to glacier outlines from previous studies and inventories over the Southern Andes region  (Rivera et al. , 2007; Davies and Glasser, 2012; Pfeffer et al. , 2014; Mouginot and Rignot, 2015). Following visual assessment, RGI5. 0 was selected as the best existing outline, corresponding to the ice extent of year 2001 (RGI6. 0 was not available at the time of our study). RGI5. 0 was not used "as is" but 1 REVERB. Available online at: https://reverb. echo. nasa. gov/#utf8=%E2%9C%93& spatial_map=satellite&spatial_type=rectangle was improved as explained in section Improvement of the Glacier Outlines.
METHODOLOGY Improvement of the Glacier Outlines The RGI 5. 0 glacier outline was corrected manually using latesummer and cloud-free LANDSAT, ASTER, and SPOT5-HRS images as references to adjust the position of the ice for the years 2000 and 2012. RGI 5. 0 ice divides were replaced by the ice divides from  Mouginot and Rignot (2015). A visual comparison between our final outlines and best existing previous ones can be observed in Supplementary Figure  S2. Significant errors and gaps were found in the SRTM water body data, so manual corrections were also performed. Furthermore, a cloud mask was drawn manually to exclude the cloudy pixels over the SPOT5-HRS DEM. No cloud mask was created for ASTER DEMs because the lack of correlation over the clouds leads directly to data voids in the DEMs.
e yr -1 for ASTER_trend). This difference is mainly related to the amount of data gaps, higher in SPOT-SRTM. The distribution of data gaps coincides over the NPI plateau (∼1,200-1,700 m a. s. l. ) for both methods. Yet, over some glaciers in the north-east margins of the NPI, specifically Grosse, Bayo, and Exploradores glaciers, less than one-third of their area is covered with the SPOT-SRTM method due to clouds in the SPOT5 images. Coverage is also restricted on small steep glaciers like glacier Pissis in the south, where a high percentage of data gaps exists over the SRTM DEM. These latter glaciers are those for which the mass balance rate differs most between the two methods. The redundancy of the acquisitions used in the ASTER_trend method overcomes this issue. The 2000-2012 Ṁ are in agreement within error bars between the two methods for all glaciers with coverage larger than 66%.
Specific cases where Ṁ do not agree within error bars correspond to glaciers poorly sampled in the SPOT-SRTM method due to clouds or SRTM data gaps. The three largest glaciers of the icefield (51% of the total area) present Ṁ differences smaller than 0. 2 m w. e. yr -1. Availability of the data is also important to consider when comparing the two methods. A strong advantage of the ASTER_trend method is that it is based on freely and nearly globally available ASTER L1A stereo images. SPOT5 stereo images were only acquired over specific areas and are not yet freely available. Thus, the ASTER_trend has the potential to be extended to wider areas.
s. l. ; <1% of the icefield). For individual glaciers, larger differences exist between the two methods, especially when the data coverage is more limited. By definition the SPOT5-SRTM estimate does not cover the area where clouds are present in the SPOT5 stereo images. The ASTER_trend method is less prone to this effect because it relies on numerous (>100) ASTER DEMs. Both methods exhibit data gaps in the upper accumulation areas due to the lack of texture in the optical stereo images. This issue could be solved in the future by using images with higher spatial resolution and better radiometric depth. Here we have provided insight into the effectiveness, advantages and disadvantages of two methods for estimating the geodetic mass balance rates of Patagonian glaciers and icefields. Both methods lead to coherent and satisfying results, though the free availability of the extensive archive of ASTER stereo images is a strong advantage.
ACKNOWLEDGMENTS ID acknowledges a Ph. D. fellowship from the France Space Agency (CNES) and the Région Occitanie. Etienne Berthier acknowledges support from the CNES and the Programme National de Télédétection Spatiale (PNTS, http://www. insu. cnrs. fr/pnts), grant PNTS-2016-01. ASTER images are courtesy of NASA/METI/AIST/Japan Space systems and U. S. /Japan ASTER Science Team. SPOT5 images and DEMs were provided by the SPIRIT project  (Korona et al. , 2009) , funded by CNES. This work could not have been performed without the GLIMS project that allowed populating a vast archive of freely available ASTER stereo images over glaciers. We also acknowledge the Ames Stereo Pipeline developer teams for their help and Vincent Favier for his comments on an earlier version of this manuscript. We are grateful to Ken Moxham for reviewing the English.
Data Analysis For analysis purposes, the 49 participating countries and regions were divided into 3 categories using the United Nations' HDI classification (low or medium, high, and very high HDI) described above. Sociodemographic data that are available online were compiled to identify the characteristics of the participating countries. Descriptive statistics (average grade and standard deviation) were calculated after converting categorical variables (letter grades) to interval variables (see corresponding numbers in Table  2 ), and the incomplete (INC) grades were converted into null values, which were treated as missing values. Averages were calculated by country, indicator, and category of HDI from the interval values, and the floor (the number rounded down) was converted back to a letter grade.
Acknowledgments The authors would like to acknowledge the then  Active Healthy Kids Canada  (now ParticipACTION) for developing the Report Card methodology, and the  AHKGA Executive Committee  for modifying and standardizing the benchmarks and grading rubric and leading this international initiative. We are grateful for all the hard work by each participating country's Report Card Work Group and Leaders and all other members of their Report Card Committees. We also want to thank  Evan Turner  for his work on the compilation of the sociodemographic characteristics of the participating countries.
Microbiological Tests. The material to microbiological tests included nasal and pharyngeal swabs collected from patients before a planned vaccination against S. pneumoniae (0m-1st test) and 1-2 months after vaccination (Im-2nd test). The swabs were collected using a kit with a transport medium, and next a culture of microorganisms was performed on the following media: Columbia agar with 5% sheep blood, Mannitol salt agar, McConkey, Chocolate agar, and Sabouraud (bioMerieux, France), and the phenotypic identification with kits: the APINH, the APIStrep, the APIE, the APINE APIStaph, and the API AUX (bioMerieux, France). The numeric code of the identified strain was read out with the apiweb™ program (bioMerieux, France). The tests were conducted according to the routinely applied microbiological diagnostics.
Author Manuscript This article is protected by copyright. All rights reserved To investigate the evolutionary history of anemone-associations in fishes, we first compiled records of fishes that associate with anemones from the literature (table  S1 ). We recorded two pieces of information for each anemone-associated fish species: i) the ontogenetic stage at which the species had been observed to associate with anemones, and ii) whether the association was facultative or obligate. This resulted in a dataset of 88 species across 16 families that could be placed into three possible character states: i) "juvenile-only facultative" (n = 19) -facultative use of anemones only recorded in juvenile individuals (average max body size = 332. 0 mm ± 34. 8 mm); ii) "facultative" (n = 22) -facultative use of anemones recorded in both juvenile and adult individuals (average max body size = 82.
2018) , which we pruned down to the most inclusive clade that represented all families that contain species that have been recorded to display mutualistic relationships with anemones. The resulting phylogeny included 16 families within the Acanthopterygii. There were 18 species in our dataset that were not represented in the pruned phylogeny. To incorporate uncertainty regarding the placement of these unsampled tips, we generated a set of 1000 phylogenies and grafted the unsampled tips to their most likely sister species or clade based on previously published information (table  S2 ). The tips were grafted with a branching point drawn at random along the grafted lineage using the bind. tip function in the R package PHYTOOLS v0. 6-2 20  (Revell 2012). The final phylogeny included 1956 species.
Author Manuscript This article is protected by copyright. All rights reserved reversible or irreversible transitions between mutualist states. In each analysis the transition (Q) matrix was fitted to the tree and data using the 'fitML()' function of PHYTOOLS. For each model, 1000 stochastic character histories were simulated across the 1000 set of grafted trees (one map per tree). The irreversible model performed slightly better when compared by AIC score (IRR: 675. 22, SYM:676. 56); but AIC weights could not separate the two models. To allow for possible reversal from mutualist to non-mutualist, we chose the SYM model. The 1000 simulated character histories were summarised to explore the number of independent origins of anemone use across the tree and number of transition between states. This was done for three variations on the mutualism state coding: one where the unknown life history stage mutualist species were coded as "facultative" as adults, one where there were coded as "unknown", and one where equal probability were given to the species being facultative as juvenile-only or as juveniles and adults.
The final coding scheme allowed the stochastic analysis to predict which state the unknown facultative tips were likely to be in. Finally, to investigate the effect of body size on association type, we retrieved adult body lengths from fishbase. org. Out of the 1956 species tree  (Rabosky et al. 2018 ) there were 50 species for which body size data were not available. As these species were non-mutualists and phylogenetically distant from any transition to any mutualism category, we pruned them from the tree prior to analyses. We used phylogenetic generalised least squares regression (PGLS) to explore whether maximum adult body size (TL) varied among species that display mutualism behaviour with anemones as juveniles or as juveniles and adults, and whether there was a relationship between maximum body size and association type (facultative, juvenile-only facultative, obligate and unknown).
As more closely related species are assumed to exhibit more similar traits  (Revell 2010) , PGLS takes into account the expected covariance structure of residuals for a given phylogeny. The correlation structure was first derived from the phylogenetic tree assuming Brownian motion model of evolution and a maximum likelihood estimate of Pagel's P (Pagel 1997) using the corPagel function of the R package ape  (Paradis et al. 2004 ). The PGLS was then run using the gls function in the R package nlme  (Pinheiro et al. 2013).
Of the 133 anemones sighted during the transects, 124 (93. 23 %) hosted threespot dascyllus, 9 (6. 77 %) hosted orange-fin anemonefish and 9 (6. 77 %) hosted no fish. Orange-fin anemonefishes were always found on anemones that also hosted threespot dascyllus, and surge damselfish were never found in association with anemones. Hawkfish, sandperch and squirrelfish were all commonly observed during transects, but their abundances were not recorded. We used a generalized linear mixed model (GLMM) with a Poisson distribution to investigate how anemone-associations varied through ontogeny in dascyllus in the R package lme4  (Bates & Maechler 2009). The full model included substrate type and fish size as fixed effects, and transect ID as a random effect. We used a zero-inflated generalized linear mixed model with a Poisson distribution to investigate whether fishes of different sizes associated more with anemones of different sizes in the R package glmmTMB  (Magnusson et al.
Acknowledgements Research was conducted with approval by the  Animal Ethics Committees at University of Delaware  ( 1292-2016-0 ) and the  PSL Research University  ( IRCP-2016-02 ). We thank  Ricardo Beldade ,  Suzanne Mills , and  Gerrit Nanninga  for helpful discussions in the field; and the staff at  CRIOBE  for logistical help. WEF was supported by the  Australian-American Fulbright Commission , RMB was supported by an  IRCP research grant ,  PFC  was supported by the  Australian Research Council  and DLD was supported by the  University of Delaware.