dataesr/scientific-paragraphs-categorization huggingface dataset:
Database overview and choice of scaling parameters The complete ITPA SOL heat flux database presented here contains 400 probe reciprocations (thus 400 values of λ q omp ) across all devices, with the SOL entry points covering the range θ = -60° → +90° in poloidal angle (table  1 ). As mentioned in the introductory remarks of section 1, the approach is to scale the data mainly on the basis of engineering parameters only. This is both because plasma parameters for ITER limiter plasmas are not yet known and because there is still no accepted physics basis allowing projection to ITER on the basis of plasma parameters measured on today's devices. Part of the here-described database has been already used in benchmarking an analytical model  [31] , which later led to development of GBS simulation described in a companion paper  [8]  in this special edition.
It uses this ITPA database to examine the credibility of a physics based approach founded on quasi-linear turbulent transport theory describing the crossfield heat convection and can claim some success, giving hope that future theoretical progress will provide a solid physics basis for exatrapolation to ITER. The parameters used in  [8]  , Λ = 17, where 'omp' corresponds to the probe locations nearby omp) are neither well-known for ITER nor typically precisely measured in current tokamaks, due mainly to imprecise knowledge of the LCFS position. However, we find in section 4 that these parameters have indeed strong links to the database, as also expected by theory  [8]. Perhaps surprisingly, given the large uncertainty in the expected ITER omp plasma temperature and density, the ITER-predicted values of λ q omp in section 4 using the midplane parameters are also found very similar to those obtained using only engineering parameters.
A further criterion determining the choice of engineering scaling variables is the potential for an impact on the SOL width. An example is the SOL magnetic connection length, π = L q R c 95 0 ∝ B 0 /I p since this is directly linked to the parallel collisionality and the latter is known to affect SOL perpendicular transport (for example higher collisionality tends to broaden the SOL and this is favoured for longer L c at fixed SOL density  [22] ). A related parameter might also be the poloidal angle at which the SOL heat flux profile is measured on each device (see table  1 ) since SOL turbulence has been shown to be ballooning in character and varies with poloidal location on the LFS  [23] [24] [25]. The choice of parameters is also determined by the degree to which they can be precisely known for each of the tokamaks contributing to the database.
This is the case for almost all the engineering parameters mentioned above with the exception of P SOL , which relies on a good measure of the core radiated power, a quantity not always available to good accuracy on all devices and sometimes simply unavailable. This is in fact the case here, with P SOL values unavailable for 14% of the entries in the database. Note that the line averaged density, n e though it is often considered as an engineering scaling parameter and is a standard experimental measurement with good accuracy on all tokamaks, is more difficult here since the values to be expected during the ITER ramp-up/down are not known and have only been thus far estimated using transport code simulations without a realistic model of the boundary plasma. We nevertheless include n e as a scaling parameter (or normalized to the Greenwald density limit  [26] , n e,Gw =10 20 I p /(πa 2 κ) with I p in MA), but will in fact find in section 4 that the dependence of λ q omp on this variable is very weak.
We therefore arrive at a list of engineering parameters to be used in the scaling: I p , I p /A, R o , a/R 0 , P tot /V, B 0 , B POL (evaluated at the omp), q omp (or q 95 for non-circular equilibria), κ, P SOL /S LCFS , n e and θ (poloidal angle of the SOL heat flux profile measurement). With the exception of R 0 and P tot /V, the ITER values of these parameters all fall within the numerical range of the database (see figure  5 ). For reference, table  2  compiles numerical values of these parameters for ITER at the three representative values of I p used to estimate design heat fluxes for the beryllium FW panels in the ITER heat and nuclear load specifications. The ITER specification for these ramp-up/down scenarios assumes P SOL (MW) = I p (MA), including ohmic heating and allowing for some additional ECRH heating power.
The 7. 5 MA value is the maximum specified current at which the inboard and outboard midplane FW panels must be able to sustain steady state limiter plasma operation. In fact, as shown in  [1] , this equivalence of I p and P SOL means that the actual value of I p is unimportant in estimating the parallel heat flux impinging on the limiters which is completely determined by λ q. Figure  5  presents a graphical overview of the 11 devices λ q omp database, with the measured values plotted separately against each of the identified scaling parameters. The ITER parameter range is indicated in each case for the highest power operating point (7. 5 MA) listed in table 2. The database clearly covers a wide range of parameters, but it is also evident that in many cases the range covered by any individual device is limited.
This is a reflection of the fact that in many cases dedicated experiments were performed to obtain the heat flux profile data and were typically restricted in available machine time. This reduces the parameter range which can be covered. Moreover, in many devices, a large range is simply not accessible for limiter discharges. The exception is Tore Supra, whose points fill ~30% of the database, with strong variations of λ q omp on I p , input power and density. However, on Tore Supra these parameters are strongly correlated (within the database span the correlations among Ω I P q L , , , p 95 c are all above 75%). Therefore, the influence of each alone on λ q omp is ambiguous and thus cannot be used for ITER prediction.
Scalings and recommendations for ITER The scaling exercise has been performed in the standard way, seeking a mathematical expression ( ) λ = … f p p p , , , q omp 1 2 15 , with 15 parameters (see table  2  or 3). In order to include the poloidal angle dependence, transformation is made into the variable (3 + cos(θ)) which is always positive, vertically symmetric and for which the ratio LFS/HFS = (3 + cos(0))/ (3 + cos(π)) = 2 is finite. Even with this transformation, however, the following regression finds no statistically significant dependence on the poloidal angle. A power law form for the scaling is chosen because all of the parameters are positive definite and some span across more than a decade in the database. Since standard statistical toolboxes for multi-parameter fitting (using the weighted linear least-squares with robust standard errors as the criterion for best fit) are in the form of linear regression ∑ α = y p i i instead of a power law ∏ = α y p i i i , the function ( ) λ = … f p p p , , , q omp 1 2 15 must first be expressed in logarithmic polynomial form ∑ λ α = = p log l og q i i i omp 0 15 (3) which corresponds to a power law.
We then use the simple statistical toolbox Gretl [27] to look for scalings. A more appropriate technique would perhaps be the principal component analysis (using the singular value decomposition algorithm). However, it is unnecessarily complex and problematic in the case of our database with many missing values. Any parameter combination with too high mutual correlation (above ±50% marked in bold) should be excluded from scalings because the exponents in the scalings are too ambiguous. Using all available parameters within a single scaling yields a matrix close to singularity with extremely high exponents and no credible predictive capability. Instead, the following criteria are used to establish which groups of variables can be employed simultaneously in any given scaling: • As shown in table 3, many of the parameters are mutually correlated in the database.
For example, R 0 is correlated at -88% with P tot /V. Thus, increasing freely the exponent on R 0 can be nearly fully compensated by increasing the exponent on P tot /V and extrapolation to ITER would therefore be ambiguous. Similar comparisons can be drawn between other pairs of the chosen scaling variables. Within a single scaling only those parameters with low levels of mutual correlation should be used, excluding many parameter combinations. • In statistical analysis, multiplication of two uncertain numbers (assuming zero mutual correlation) combines the relative errors, ε = error/mean as ∏ ∑ α = α ⎛ ⎝ ⎜ ⎞ ⎠ ⎟ ε ε p i i i i i 2 i implying that the overall error is mostly determined by the most uncertain parameter and the fewer free parameters, the smaller the overall error. This suggests that the number of parameters within one scaling should be as small as possible, though errors on the main plasma parameters are usually unknown in the database and are assumed to be small (e.
Discussion of the scaling results An uncertainty factor is present in the sense that the input data provided for the scaling exercise have been processed by individuals from each participating machine, an issue which complicates matters due to the different methods by which Langmuir probe characterstics are fitted and the varying probe techniques used. Most of the reasonable engineering scalings (e. g. figure  6 (a)) slightly overestimate the experimental values from TEXTOR, DIII-D and FTU. In contrast, systematic underestimation is found for C-Mod, EAST, COMPASS (LFS measurement) and for some scalings also for JET. It is not possible to conclude that a specific 'human factor' leads to any systematic bias in the scalings, though a single individual (Horacek) did process the raw data from all COMPASS, TEXTOR and JET and this did not lead to any systematic shift with respect to the scalings.
Conclusions To hide misalignments between neighbouring plasma-facing panels on the ITER main chamber FW, the panels are toroidally shaped. This shaping increases the heat flux on the panel compared with an ideal cylindrical surface and this is a key disadvantage given the use of beryllium as armour material and the use of active cooling, which limit the steady state power handling capacity for limited plasmas which will characterize the start-up and ramp-down phases of all ITER plasmas. The shaping is thus optimized by careful choice of SOL parallel heat flux decay lengths. For the inboard midplane panels, it is now thought that two values of λ q imp are required to account for a narrow heat flux feature in the vicinity of the LCFS and a broader main SOL width. The latter was originally specified at λ q imp = 50 mm for the design of the ITER toroidal shaping, based on a restricted (three tokamaks) experimental database of L-mode divertor plasma measurements.
This paper brings together and provides the statistical analysis of a significantly enlarged (eleven tokamaks) database, with dedicated measurements of λ q extracted from Langmuir probe radial profiles of electron density and temperature obtained from inner wall limiter plasmas during experiments executed under the auspices of the ITPA, Divertor and SOL Topical Group. Multi-parameter regression analysis of data from ~400 probe reciprocations has yielded nine highly credible scalings using both engineering parameters and on theory-based  [8]  dimensionless plasma parameters. All of the scalings yield similar values for λ q imp when extrapolated to Table  4. Overview of the most credible scalings (valid for tokamak inboard-limited plasmas) and consequent ITER predictions of the main SOL heat flux width at the outboard midplane for the three reference ITER plasma currents.
Example of radial profile fitting of ion saturation current j sat , T e and q || ~ j sat T e from the JET vertical reciprocating probe. Error bars resulting from the fits are taken for further processing in figures 5 and 6. Fit extrapolation up to LCFS is required for calculation of omp LCFS plasma parameters gbs omp ν , ρ * , β omp. Even though steep gradients have been observed in the near SOL region [1, 20]  at the high field side (HFS), in our database it is never present outside the LCFS, making simple exponential extrapolation credible. This often leads to large error bars (shown by the vertical bars at rr LCFS = 0, depending especially on how close to LCFS the probe could penetrate) in these variables, which is also taken into account in further processing.
Acknowledgments This work was supported in part by the projects of  Czech Science Foundation   GA CR P205/12/2327 ,  GA15-10723S  and  MSMT LM2011021 , the  US DOE  under  DE-FG02-07ER54917  and  DE-AC02-09CH11466 ,  DE-FC02-04ER54698. This work has been carried out within the Framework of the  EUROfusion Consortium  and has received funding from the  Euratom  research and training programme  2014-2018  under grant agreement number  633053  withing the European Union's  Horizon 2020. The views and opinions expressed herein do not necessarily reflect those of the  ITER Organization and of the European Commission. ITER is the Nuclear Facility INB-174. We acknowledge useful discussions with  Renaud Dejarnac ,  Federico Halpern  and  Petr Dobias.
igure 4. 4 Example of radial profile fitting of ion saturation current j sat , T e and q || ~ j sat T e from the JET vertical reciprocating probe. Error bars resulting from the fits are taken for further processing in figures 5 and 6. Fit extrapolation up to LCFS is required for calculation of omp LCFS plasma parameters gbs omp ν , ρ * , β omp. Even though steep gradients have been observed in the near SOL region [1, 20]  at the high field side (HFS), in our database it is never present outside the LCFS, making simple exponential extrapolation credible. This often leads to large error bars (shown by the vertical bars at rr LCFS = 0, depending especially on how close to LCFS the probe could penetrate) in these variables, which is also taken into account in further processing.
We recorded i) water temperature, ii) discharge and iii) daylight hours, and applied these to the entire study site as 'global' parameters in the model (see Supplementary Table  S1 ). We also recorded i) predator densities and collected data on invertebrate densities to estimate ii) invertebrate prey abundance and applied these as stretch-specific parameters in the model (see Supplementary Table  S2 ). We measured body mass and specific growth rates (SGR) from four electric fishing surveys. We caught a total of 1453 YOY Atlantic salmon, 283 YOY and 81 1+ brown trout of which 94, 96, and 79 were marked recaptures respectively. Over both flows, mean fish body mass increased and there was greater population variation in SGR during the MLF period (Fig. 2 ). YOY Atlantic salmon SGR showed the greatest pattern of change from the NF to MLF period while more YOY Atlantic salmon recorded slower growth rates in the latter period (Fig.
05% and 0. 01% for YOY Atlantic Salmon, YOY and 1+ brown trout respectively. Model validation. Pattern-orientated validation  29, 40  was applied using time series data of: i) body mass, ii) specific-growth rates, distribution at the iii) stretch and iv) patch scale for YOY Atlantic salmon, YOY brown trout and 1+ brown trout. Model outputs were selected to match observations by date and location when data was collected relative to the study period. Bayesian estimation of the probability of the difference between observed and predicted growth rates were calculated using the R package BEST. R  68. Fish mass and specific growth rates. We compared observed and predicted fish body masses (g) at the time points when the last three fish surveys were performed. We also compared SGRs of real and virtual fish at the end of both the NF and MLF periods as: s where BM = body mass (g), t = time (days) at the start (s) and end (e) between recaptures.
The "author pays" model This is the model that applies when authors or their institutions of affiliation or funding bodies pay the publisher an article processing charge to make the article openly and freely accessible to any reader. It is an alternative to the "reader pays" and "sponsor pays" models. (Source: INIST Glossary) The "reader pays" model 25 This is the traditional model in publishing, and works by subscription. Readers have access only to journals and books for which they, or more often their institution, have purchased a subscription from one or more publishers. It is an alternative to the "author pays" and "sponsor pays" models. For several years now, the scientific community involved in public research has been arguing for the need to create a legal and organisational framework for access to scientific and technical data and information in the digital world, in particular data from its own research activities.
However, this approach is relatively time consuming and targets only one taxon at a time. In this study, we assessed the feasibility of using HTS data as a quantitative metric in picoeukaryote diversity studies by comparing relative HTS read abundances with relative FISH cell counts in selected picoeukaryotic taxa. Unlike the previous studies, in which a single taxon (ciliates) or artificial communities were analyzed, this study focused on a set of highly divergent lineages found in geographically separated and unrelated microbial assemblages. Any pattern emerging from this heterogeneous and noisy data set was expected to be rather robust. We also investigated if there was a difference in community composition assessed by using environmental DNA or RNA extracts as the templates (DNA and cDNA reads, respectively), sequencing different regions of the 18S ribosomal DNA (rDNA) (V4 versus V9) or using different HTS platforms (454 versus Illumina).
For flow cytometry counting of photosynthetic picoeukaryotes, aliquots of 1. 5 ml were fixed with a mix of paraformaldehyde and glutaraldehyde (1% and 0. 25% final concentrations, respectively), frozen in liquid nitrogen, and stored at Ϫ80°C until processed. Picoeukaryote cell abundance by DAPI staining and flow cytometry. The total cell abundance of picoeukaryotes was estimated in DAPIstained filters. Cells were counted with an epifluorescence microscope (Olympus BX61) at 1,000ϫ under UV excitation, changing to blue light excitation to verify the presence or absence of chlorophyll autofluorescence (phototrophic and heterotrophic cells, respectively). A transect of about 13 mm was inspected, and cells were classified in size classes: 2 m, 3 m, 4 m, 5 m, and Ͼ5 m. All data reported in the study referred to cells within the two smaller size classes (2 to 3 m), which accounted on average for 82% of the cells.
Cell abundance of photosynthetic picoeukaryotes was determined in a FACSort flow cytometer by using the red fluorescence signal (chlorophyll) after excitation in a 488-nm laser and the side-scattered light of each particle. Fluorescent microspheres (0. 95-m beads) were added as an internal standard (at 10 5 beads • ml Ϫ1 ). Data were acquired for 2 to 4 min with a flow rate of 50 to 100 l • min Ϫ1 using the settings previously described  (34). Cell abundance of specific picoeukaryote taxa by TSA-FISH. The specific oligonucleotide probes used targeted several picoeukaryote taxa: NS4 and NS7 targeted the uncultured clades MAST-4 and MAST-7; CRN02 and MICRO01, the species Minorisa minuta and Micromonas spp. ; PELA01, the class Pelagophyceae; and ALV01, the environmental clade MALV-II (Table  2 ).
These probes have been published in other studies (see references cited in Table  2   [26] [27] [28] [35] [36] [37] ) except NS7. Probe NS7 was designed here with ARB  (38)  and targeted 91% of the 192 sequences from MAST-7 available in GenBank; it had 1 mismatch with the remaining MAST-7 sequences and had at least 2 central mismatches with nontarget sequences. Probe NS7 gave a better signal when combined with oligonucleotide helpers contiguous to the probe region (NS7 helper A: AACCAACAAAATAGCAC; NS7 helper B: CCCAACTATCCCTATTAA) that were added to the hybridization buffer at the same concentration as the probe. We tested a range of formamide concentrations to find the best hybridization condition, and we checked that the probe gave a negative signal with a variety of nontarget cultures. Finally, a probe targeting all eukaryotes (EUK502, 37) was also used.
0015% H 2 O 2 ) containing Alexa 488-labeled tyramide (4 g • ml Ϫ1 ) by incubating in the dark at room temperature for 30 to 60 min. Filter pieces were transferred twice to a PBS bath in order to stop the enzymatic reaction and air dried at room temperature. Cells were countersained with DAPI (5 g • ml Ϫ1 ), and filter pieces were mounted on a slide. Targeted FISH cells were counted by epifluorescence under blue light excitation and checked with UV radiation (DAPI staining) for the presence of the nucleus. Cells labeled with the probe EUK502 were counted using the same size classes as for DAPI counts. Data reported refer to cells of 2-to 3-m sizes, which accounted on average for 84% of the cells. HTS by 454 and Illumina. HTS data derive from papers published during the BioMarKs project (http://www.
biomarks. eu/). Total DNA and RNA from 13 picoplankton samples were extracted simultaneously from the same filter. For RNA extracts, contaminating DNA was removed, and RNA was immediately reverse transcribed to cDNA. Data for the 454 sequencing are derived from the work by Massana et al. (32)  and used the eukaryotic universal primers TAReuk454FWD1 and TAReukREV3  (40) , which amplified the V4 region of the 18S rDNA (ϳ380 bp). Amplicon sequencing from DNA and cDNA templates was carried out on a 454 GS FLX Titanium system (454 Life Sciences, USA) in Genoscope (http://www. genoscope. cns. fr). The complete sequencing data set is available at the European Nucleotide Archive (ENA) under the accession number PRJEB9133. Data for the Illumina sequencing are derived from the work by Logares et al.
(33)  and used the eukaryotic universal primers 1398f and 1510r  (41) , which amplified the V9 region of the 18S rDNA (ϳ130 bp). Paired-end 100-bp sequencing was performed using a Genome Analyzer IIx (GAIIx) system located at Genoscope. Only RNA (cDNA) samples were sequenced with Illumina. Sequences are publicly available at MG-RAST (http://metagenomics. anl. gov) under accession numbers 4549958. Sequence analysis of HTS reads. HTS reads by 454 and Illumina were quality checked following criteria similar to those detailed in the original papers  (32, 33). After the quality control, chimera detection was run with UCHIME  (42)  and ChimeraSlayer (43) using SILVA108 and PR2 (44) as reference databases. The final curated reads were clustered into operational taxonomic units (OTUs) by using UCLUST 1.
2. 22  (45) , with similarity thresholds of 97% for V4 reads and 95% for V9 reads. Representative reads of each OTU were taxonomically classified by using BLAST against SILVA108, PR2, and a marine microeukaryote database  (46). After the taxonomic assignment, metazoan OTUs were removed. From the complete OTU tables for 454  (32)  and Illumina  (33)  data sets, the samples targeting the picoplankton were extracted: 13 samples for DNA-V4, 13 samples for cDNA-V4, and 9 samples for cDNA-V9. Then, OTUs corresponding to taxa typically larger than 3 m (Dinophyceae, Ciliophora, Acantharia, Diatomea, Polycystinea, Raphidophyceae, Ulvophyceae, The relative abundance of the picoeukaryotic groups of interest was retrieved from these taxonomically classified OTU tables, by dividing the number of reads of the specific OTUs corresponding to the groups of interest by the total number of reads in the sample.