dataesr/scientific-paragraphs-categorization huggingface dataset:
The quality of a tracer dataset also depends on the absolute number of mobile recovered tracers. Although there is no rule of thumb for the sample size of tracer studies available in the literature, it clearly appears that low recovery rates obtained in large rivers have been compensated by the seeding of larger tracer populations, leading to equivalent mean sample sizes in small and large rivers (Figure  3b ). Another recent strategy is based on a mixing of active and passive RFID to better capture frontrunners with active RFID (due to their generally high recovery rate) while maintaining a high number of recovered tracers with passive RFID  (Boutault, 2020). A bootstrapping (with replacement) procedure has been proposed to estimate the geopositioning error of tracer cloud centroids as a function of the resampling rate of recovered tracers  (Piégay et al.
A toolbox was recently developed for maximizing information from a for RFID (8 years;  Bradley, 2017) , the spatial scale of RFID experiments includes many tracer plumes with km-length scales, some of them having been obtained for very short inter-survey periods (e. g. Misset et al. , 2020). Such extensive tracer plumes with magnets (maximum distance >1 km) are quite rare and correspond to long-term experiments  (Ferguson et al. , 2002; Haschenburger, 2013). To the best of our knowledge, the only reported cases of rapid bedload dispersion in the 'before RFID' tracing literature were obtained from F I G U R E 3 Recovery rates (a) and number of recovered tracers (b) of passive and active RFID bedload surveys as a function of river channel width from data compilation of field studies (see Table  S1 ).
| Effects of flow conditions on transport distances One of the primary controls of bedload dispersion is hydraulic forcing. Excess specific stream power was early used in meta-analysis of tracer datasets for scaling the mean distance of transport with flow conditions  (Hassan et al. , 1992; Sear, 1996). This was done by considering the peak discharge between two tracer surveys relatively close in time. Many scaling laws of that type have been reported from eventbased RFID tracing studies in different fluvial environments  (Arnaud et al. , 2017; Gilet et al. , 2020; Houbrechts et al. , 2015; Lamarre, Roy, 2008b; Papangelakis, McVicar, et al. , 2019; Peeters et al. , 2021; Rainato et al. , 2020; Schneider et al. , 2014)   S1  and S2 ). The scatterplot includes all tracer surveys with available information on recovery rates and number of recovered and mobile tracers (n = 214 for RFID and n = 29 for magnets).
The power law fit is based on the whole dataset (n = 243). The grey shading corresponds to the 95% confidence interval of the regression. compilation allows investigating two normalization factors of bedload dispersion: channel width and median grain-size of the channel surface. Both allow a normalization by channel size, with an increase of channel width and a decrease of grain-size with drainage area. The dimensionless stream power of  Eaton and Church (2011)  was used as the flow predictor of the normalized distance of transport. This was computed as ω Ã ¼ ω=ρ gRD 50 ð Þ 3=2 , ð1Þ with ω the unit stream power, g the acceleration of gravity, ρ the water density, R the submerged specific gravity of sediment and D 50 the median surface grain-size of the channel. The best empirical relation was obtained with the transport distance normalized by the median channel grain-size (L/D 50 = 4954 T A B L E 2 Summary statistics of linear regressions on log-transformed values of mean distance of transport versus excess unit stream power using data presented in Figure  7a.
Step pools Plane beds Riffle pools Best fits L = 0. 026 (ω-ω 0 ) 1. 076 L = 0. 039 (ω-ω 0 ) 1. 315 L = 0. 578 (ω-ω 0 ) Note: L: mean distance of transport (m); ω-ω 0 : excess unit stream power (W m À2 ). F I G U R E 7 Stream power scaling laws from data compilation of bedload tracing surveys: (a) updating of the scatterplot from  Hassan and Bradley (2017)  between the mean distance of transport and the excess unit stream power, including tracing datasets collected in step-pool  (Gintz et al. , 1996; Lamarre & Roy, 2008b; Lenzi, 2004; Rainato et al. , 2020; Schneider et al. , 2014) , plane-bed  (Gilet et al. , 2020; Houbrechts et al. , 2015)  and riffle pool channels  (Arnaud et al. , 2017; Haschenburger & Church, 1998; Houbrechts et al. , 2015; Liébault et al. , 2012; Papangelakis, McVicar, et al.
Datasets with inter-survey duration >1 year or with number of recovered and mobile tracers <30 were excluded. The black line represents the best fit, with 95% confidence interval of the regression in grey. ω* 1. 66 , R 2 = 0. 38, p value < 0. 001, 95% CI of exponent: 1. 27-2. 05, 95% CI of intercept: 2355-10 415) (Figure  7b ). This relation is based This also means that the classification of channel morphology used here as an indicator of sediment regime (supply vs transport-limited) is may be too rough to account for the gradient of sediment supply conditions of the compiled study sites. Of particular interest would be to test quantitative proxies of sediment supply based on the Shields number, as proposed by  Church (2006) , but this implies collecting more information about dominant flood discharges (bankfull discharges or 2-year flood discharges) of the study sites.
It remains quite uneasy to collapse the data along one single relation. A better collapsing should be possible by considering the full hydrological forcing during periods between tracer surveys, as proposed by  Phillips et al. (2013) , who introduced the concept of the dimensionless impulse for analysing tracer datasets. This approach was already successfully tested on several RFID tracer plumes  (Arnaud et al. , 2017; Chardon et al. , 2021; Gilet et al. , 2020; Imhoff & Wilcox, 2016; McQueen et al. , 2021; Olinde & Johnson, 2015; Peeters et al. , 2021; Plumb et al. , 2017; Schneider et al. , 2014) , and a meta-analysis of those data would be certainly very instructive. Hydrological time series must be shared for making this possible, which is currently not the case for most of reported tracer surveys.
, 2021). The CH92 scaling procedure and their hybrid forms are based on a normalized transport distance (L i /L D50 , with L i the mean distance of the size group i, and L D50 the mean distance of the size group including the median surface grain-size of the bed material) and a normalized grain-size (D i /D 50 , with D i the tracer grainsize, and D 50 the median grain-size of the surface or subsurface bed material). The CH92 scaling law exhibits an exponential decreasing form. Most of the RFID tracing datasets showed good agreement with the CH92 relation, with full mobility (i. e. , the transport distance is independent of grain size) for grain sizes up to two to four times the D 50  (Casserly et al. , 2020; Chardon et al. , 2021; Mao et al. , 2020; McVicar & Roy, 2011; Papangelakis, McVicar, et al.
5 years) to exclude event-based surveys for which the extrapolation of travel distances to the annual scale may not be representative. It is interesting to see that annual travel distances of most of the riffle-pool datasets are effectively less or equal to the typical length of the riffle-pool-bar unit (considered here as equal to 5. 9 the channel width, as proposed by  Keller & Melhorn, 1978). However, several riffle-pool datasets show that the annual travel distance can strongly exceed this length scale, and this is especially the case of the multithread riffle-pool group. Morphological controls on bedload dispersion in riffle-pool channels are more or less efficient depending on other factors, the sediment supply being likely a key forcing to consider. As previously said, the integration of quantitative proxies of sediment F I G U R E 8 Mean annual travel distance as a function of the channel width.
This scatterplot includes all compiled RFID and magnetic tracer surveys with available information on the duration of the inter-survey period and the mean travel distance (see Tables  S1  and S2 ). Datasets with inter-survey duration <0. 5 years were excluded. The black line corresponds to the best fit. The grey shading corresponds to the 95% confidence interval of the regression. The green line corresponds to the mean length of riffle-pool-bar units, considered as equal to 5. 9 times the channel width (following  Keller & Melhorn, 1978). supply should potentially improve our understanding of the high variability of annual travel distances reported in tracer studies. Morphological controls can also be considered by looking at the effect of initial conditions on tracer dispersion. Comparison of transport distances of tracers deployed in different morphological units generally shows a significant effect of initial conditions, with a stronger mobility of tracers deployed near or in low-flow channels, as compared to those deployed on gravel bars  (Brenna et al.
, 2017; Brousse, Arnaud-Fassetta, et al. , 2020; Chapuis et al. , 2015; McDowell et al. , 2021; McQueen et al. , 2021; Milan, 2013). Such intensive reach-scale monitoring has been very useful for interpreting morphological processes such as bar downstream migration or lateral accretion in terms of individual particle deposition and burial  (McQueen et al. , 2021). These morphological effects are known to produce multi-modal path length distributions that can be reproduced by Bayesian survival process models, as recently demonstrated with several RFID datasets  (McDowell et al. , 2021; McDowell & Hassan, 2020).
We now need to combine the recovery rate with other quality criteria, such as the RFID survey density and the RFID recovery density to provide a spatial resolution of the survey and better identify the causes for tracer loss. The 'efficiency score' proposed by Cassel et al. (2020) is based on the surveying effort. It offers an interesting framework to discuss the overall cost of RFID tracing (including equipment and surveys) and therefore support users in the choice of an RFID technology and survey protocol adapted to their study site. ACKNOWLEDGEMENTS Many RFID field experiments reported in this review paper have been undertaken in the framework of the LTER ZABR,  CNRS- INSU-SNO Draix-Bléone ,  Rhône Sediment Observatory (OSR ) programme , and  EUR H2O'Lyon  ( ANR-17-EURE-0018 ) of  Université de Lyon (UdL).
The  Draix-Bléone Observatory  is funded by  INRAE ,  CNRS-INSU  and  OSUG  and is part of the  French network of Critical Zone   Observatories OZCAR , which is supported by the  French Ministry of Research, French Research Institutions and Universities. David Milan ,  Louis Gilet  and  Fabien Boutault  are acknowledged for personal communications about their RFID tracer datasets. Valentin Chardon  and  Laurent Schmitt  are acknowledged for sharing Figure 11. We acknowledge the thorough reviews by  Riccardo Rainato ,  Joel Johnson ,  Georgie Bennett , and two anonymous reviewers.
The excess stream power diagram from the meta-analysis of Hassan & Bradley (2017)  was updated with additional event-based surveys from our data compilation (Figure7a). The morphological gradient appearing in fig. 6. 1 of Hassan and Bradley (2017)  is confirmed, with higher transport distances for transport-limited conditions (riffle-pool > plane-bed > steppool). Significant statistical relationships have been obtained for each morphological group (Table2), but with a very high intra-group scatter, typical of the variability of bedload responses to flow conditions. Additional factors of variability are related to morphological effects on bedload dispersion (see Section 3. 1. 4). It is interesting to see that many RFID datasets in riffle-pool channels plot within the gravelover-sand group from magnets, which draws an upper envelope of bedload dispersion.
on a subset of surveys with populations of mobile and recovered tracers >30. Tracer surveys associated with long time periods (intersurvey duration > 1 year) were also excluded in order to consider only surveys for which tracer mobility can be reasonably associated with one peak discharge. This analysis reveals contrasted patterns of bedload dispersion between morphological groups, with a lower relative bedload mobility in step pools, contrary to what has been obtained by Vázquez-Tarrío et al. (2019). Braided and plane-bed channels show the highest normalized bedload mobility. Plane-bed datasets with very high mobility correspond to a coarse mountain stream of Idaho (Olinde & Johnson, 2015) , where a plausible explanation of extreme travel distances is the low vertical mixing of tracers during the survey period.
addressed at the scale of individual tracers, by comparing shear stress conditions experienced by mobile and immobile tracers. By computing the shear stress at initial tracer positions at the time of the peak discharge during a period between two tracer surveys and by considering two populations of tracers (mobile vs immobile), one can calculate the probability of grain entrainment as a function of shear stress. This was successfully done using 2D hydraulic modelling of a flow event mobilizing a large population of RFID tracers (Roberts et al. , 2020). A simplified approach was used in the wandering Bouinenc gravel-bed river (RFID dataset presented in Liébault et al. , 2012)  by considering 1D shear stress computed from high-water marks along cross-sections of tracer seeding. The comparison of shear stress experienced by mobile and immobile tracers at the time of the peak discharge allows to propose a range of critical Shield numbers offering the best discriminant domain between the two groups (Figure10).
these two regulated gravel-bed rivers, very large populations of PIT tags (n = 1000 to 1500) were introduced in the replenished sediments with recovery rates of 11-43% for the Rhine and 7-67% for the Rhône. A replenished site of the Rhine has been surveyed seven times between 2009 and 2017, including a 15-year flood. Translation of the sediment pulse and then dispersion and fragmentation were observed through time, with preferential deposition on riffles (Figure 11; Chardon et al. , 2021). A multiple regression model was applied to the Rhône dataset including 35 RFID surveys (Vázquez-Tarrío, Arnaud, et al. , 2021). It showed promising results to estimate mean transport distances, making it therefore possible to estimate the time needed to export a given volume of sediments from a restored river reach, as well as the volume potentially trapped in the reach.
various ways fluvial geomorphologists initially developed RFID tracking techniques to provide new knowledge, predict transport distances and diagnose river behaviour and responsiveness are inspiring in terms of collective intelligence because successive publications are steps to improve initial findings. A lot of progress has already been done; however, there are still rooms to improve the use of the techniques. Four key challenging issues can be stated. 1. Optimizing field and statistical protocols for RFID bedload tracing datasets This includes methodological analysis of sample sizes required for different objectives, that is, assessing if n RFID tracers recovered are enough or not to achieve a robust reconstruction of transport distance distributions for different types of fluvial environments.
Such field experiments based on high-frequency surveys or continuous monitoring of virtual velocity are still quite uncommon and should be replicated in the near future to confirm their interest for indirect bedload transport monitoring. Active UHF RFID tracers offer an efficient solution for bedload tracing and active layer measurement of large gravel-bed rivers, but more field experiments are needed for validating bedload flux estimates. There are also rooms to combine techniques such as active and passive RFID tracers to maximize the detection of frontrunners and tracer cloud centroids. The merging of RFID datasets with other sensors (e. g. , seismic.
4. Fostering future meta-analysis of RFID bedload tracing experiments Meta-analyses are very valuable for predicting tracer transport distances and exploring the variability of bedload dispersion in heterogeneous fluvial environments. The compilation of RFID datasets delivered with this review paper allows us to share information of 459 tracer surveys for a total of 125 study sites encompassing a great variety of fluvial environments and flow regimes. To provide new results in this domain, there is clearly a collective organization to initiate in terms of standardization of data collection protocols and datasharing. Summary information is generally (but unfortunately, not systematically) provided by authors in primary research papers, for example, type of RFID tag, tracer particle GSD, seeding and survey dates, number of deployed tracers, recovery rates, mobility rates, mean and maximum travel distances, study site characteristics including bed slope, channel width and flood discharges (most of these terms are presented in our bedload tracing database; see the supporting information).
(2009b). Saturn is detected differentially on top of the IR-bright sky background via the chopping and nodding technique, subtracting contributions from the sky and telescope. Image navigation and geometrical registration requires fitting a planetary silhouette over the image to determine latitudes, longitudes, and emission angles, before producing an equirectangular map of the data. Given the low reliability of absolute calibration via stellar comparison on nonphotometric nights, data were calibrated by scaling the observed flux at low latitudes (and excluding ring absorption) to a low-latitude average of Cassini/CIRS data, as described by  Fletcher et al. (2009b)  and further detailed below. Whilst such a calibration is crude, it enables the creation of a consistent dataset for studying relative brightness changes, but would not capture absolute changes in Saturn's global temperatures over time.
Subaru/COMICS 2005-2020 VISIR observations were supplemented by images from the Cooled Mid-IR Camera and Spectrograph (COMICS) on the Subaru telescope of Japan  (Kataza et al. , 2002; Okamoto et al. , 2003)  between 2005 and 2020, a subset of which are shown in Fig. 1  and 2. The COMICS 320 × 240 array had a coarser plate scale of 0. 133 ′′ /pixel compared to VISIR, but a larger field of view (42 × 32 ′′ ), preventing clipping of Saturn's rings. Previously-published observations from April 2005  (Fletcher et al. , 2009b)  spanned 8. 7-24. 5 μm, but lacked a 7. 8μm filter sensitive to stratospheric methane. This was added for later datasets in each of the following years:  2007-2009; 2013; and 2017-2020. COMICS was decommissioned in 2020, and raw data are available through the SMOKA archive.
The IRTF images were supplemented in 1995 and 1996 by observations from the SpectroCam-10 instrument on the Hale 200-inch at Palomar (described by  Orton et al. , 2008). Two additional sources of IRTF data are not considered as part of this study -imaging from the MIRSI instrument between 2003 and 2012  (Deutsch et al. , 2003) , and spectral scan maps from the TEXES instrument  (Lacy et al. , 2002)  acquired since 2002  (Greathouse et al. , 2005). The former are superseded in this study by the aforementioned VISIR and COMICS observations spanning the same time period with a superior spatial resolution. The spectroscopic maps from TEXES require significantly different processing techniques to ensure a valid comparison to the imaging datasets. These extensive datasets will be the subject of a future study.
Comparison of Cassini and ground-based observations 2004-2017 In this Section, we compare observations from 8-m class observatories (VLT and Subaru) to the record of brightness temperatures and retrieved temperatures from the Cassini/CIRS instrument. We explore the ground-based data in the following subsections by (i) generating synthetic images using the CIRS database for comparison to observations of Saturn's seasonal changes (Section 3. 1); (ii) measuring brightness temperature gradients to understand Saturn's belt/zone structure (Section 3. 2); and (iii) deriving 2D temperature-pressure cross-sections from stacked filtered images (Section 3. 3). Zonal-mean brightness temperatures for COMICS and VISIR observations from 2005-2017 are shown in Fig. 5 , compared to filterintegrated brightnesses measured by Cassini.
We show a selection of four filters: 7. 8, 10. 7, 17. 6 and 18. 6 μm. These are not true zonal averages, but approximations formed by averaging within ±30 • longitude of the central meridian for each image, assuming longitudinal homogeneity. This is a good approximation for most dates except for those in 2011-13, where significant longitudinal structure was present due to the northern springtime storm  (Fletcher et al. , 2012) , as observed in Fig. 5 (e). Regions obscured by Saturn's rings are omitted for each date, although some high-latitude 'winter-hemisphere' points are included. The ground-based zonal-mean   is compared to data from Cassini/CIRS -specifically a database of spectra developed by curtailing CIRS interferograms so that they all have the same length (e. g. , 15-cm -1 spectral resolution) following  Fletcher et al.
(2017). The CIRS far-IR focal plane one was used for Q-band observations (>17 μm), whereas the mid-IR focal planes (3 and 4) were used for N-band observations (7 -13 μm). CIRS spectra were convolved with VISIR filter functions to approximate the brightness temperature as seen by the ground-based observatories in Fig. 5(e-h ). No attempt has been made to remove limb-brightening/darkening in either dataset. Fig. 5  shows the seasonal progression of tropospheric and stratospheric temperatures during the course of the Cassini mission. In the stratosphere, the southern-summer   ranges from 140 K at the south pole to 110 K at the north pole in 2005-2007, but by the end of the Cassini mission in 2017 this gradient had reversed, with peak brightness temperatures of ∼140 K at the north pole. Equatorial stratospheric temperatures at 7.
Synthetic Saturn images based on CIRS database The time series of Saturn's atmospheric temperatures and stratospheric composition, retrieved from Cassini/CIRS between 2004 and 2017 (e. g. ,  Fletcher et al. , 2016 Fletcher et al. , , 2018b)) , was used to generate synthetic images of Saturn for comparison with ground-based data in Fig. 6. In this section we focus on COMICS observations in April 2005, and VISIR observations in September 2017. The latitude, longitude, and emission angle were extracted for every pixel in each image, such that viewing geometry and detector plate scales are properly accounted for. Latitude-pressure cross-sections of retrieved temperatures, ethane, and acetylene abundances were then used to generate synthetic radiances in each pixel, using the NEMESIS suite of radiative transfer and spectral retrieval tools  (Irwin et al.
2). At 1 mbar, the peak temperatures within the SPSV (∼162 K) and the NPSV (∼150 K) are within ±5 K of the temperatures measured by Cassini/CIRS during the same periods  (Fletcher et al. , 2018b). At 110 mbar, the temperature estimates during southern summer (90±2 K) and northern summer (86 ± 2 K) are consistent with those derived from Cassini/CIRS  (Fletcher et al. , 2018b). This is not surprising, given the use of CIRS data to calibrate the ground-based data, but demonstrates the self-consistency of this approach.