dataesr/scientific-paragraphs-categorization huggingface dataset:
Therefore we use Eq. (A1), which allows us to calculate Ẽf based on our simulations. Indeed, by replacing the expression of e 0 and e f (Eq. A2), and by assuming that the bed dissipation w b depends linearly on the velocity (i. e w b f (i. t) v f (i. t) ≈ w b 0 (i. t) v 0 (i. t) ) (Eq. A2), the definition of the dissipation added due to forest is obtained (Eq. A3). So at the time step n, with t the time step, it is possible to obtain the total dissipation added by the forest Ẽf. Code and data availability. All the data used in the figures in this paper are on Zenodo at https://doi. org/10. 5281/zenodo. 6121811 (Védrine et al. , 2022). A detailed description of the MPM model can be found in a previous publication at.
Acknowledgements. We acknowledge  Perry Bartelt  and  Peter Bebi  from  SLF, Davos  for insightful discussions on the topic of avalanche-forest interaction and the detrainment approach. We also thank  Jean-Louis Gay  from the  Vaud Canton  and  Clara Streule  and  Francesc Molné  from  EPFL  for their preliminary contribution on the topic in the framework of an  EPFL Design Project  motivated by a forest fire in an avalanche-prone area (Les Diablerets). Finally, we thank  Martin Proksch  from the  Wallis Canton  and  Andreas Tegethoff from Geoformer  for constructive suggestions for the practical application of this work. Financial support. This research has been supported by the  Swiss National Science Foundation  (grant nos. PCEFP2_181227  and  CRSK-2_195914 ). Review statement. This paper was edited by  Oded Katz  and reviewed by three anonymous referees.
Statistical analysis Twenty individuals from each population were examined. The variations in time required for prey capture under various conditions (aquarium size, in darkness or light conditions) were not normally distributed, and considering the limited sample size, data were thus analyzed using non-parametric tests. The scores of the same identified animals tested under various conditions were compared using the Wilcoxon unilateral test (XLstat software). Differences between populations were analyzed using the Friedman test (XLstat software). P < 0. 05 were considered statistically significant.
METHODS Here we analyze a new dataset from the Var River-Canyon system that was collected during the Solveig III research cruise, acquired over a period of seven months, from late June 2009 to early February 2010 (Fig. 2 ; Blandin 2010). We first compare velocity data from a proximal canyon mooring to river discharge measurements, to analyze the relationship between the velocity structure of turbidity currents and associated river discharge. We then use a second mooring to trace the changes in the velocity structure of the turbidity currents down the canyon. Another, third mooring was located directly offshore the river mouth (2. 8 km), but it unfortunately was located just outside the deepest part of the canyon floor (thalweg) and consequently did not record any turbidity currents.
Most instruments on the moorings recorded at a 20-minute interval, unless stated otherwise. Station VH was located on the side wall of the submarine canyon at 121 meters water depth (mwd), and 2. 8 km from the Napoleon III measurement station at the river mouth (Fig. 1A, B ). On this mooring, there was a Seaguard Recording Current Meter (RCM) at 15 meters above the seafloor (masf), an RCM 11 at 25 masf that recorded every 5 minutes, and an Aquadopp RCM at 35 masf recording at 30 min intervals (Fig. 1C ). Station VE was deployed at 518 mwd, at a location 5. 7 km from Napoleon III (Fig. 1A, B ). Mooring VE had a turbidity sensor mounted at 15 masf (Fig. 1C ). A 300 kHz acoustic Doppler current profiler (ADCP) was mounted at 30 masf, which recorded over a series of 2-m-high bins. Besides the maximum velocity, the velocity at 25 masf is also extracted from the ADCP data to enable a direct comparison between the depthresolved ADCP data and the single depth point RCM measurements on the other moorings.
This ADCP recorded every 5 minutes, and had a 6 m vertical bin size. Sediment concentrations in the flows were also estimated using backscatter data of the 300 kHz ADCP at station VE. This provides further information on first-order flow character, but involves a significant assumption that grain size does not vary with height above the bed (see  Simmons et al. (2020)  for a detailed description of likely errors associated with this assumption). However, it is established that grain size will increase towards the bed, with grain-size stratification depending on the Rouse number and other factors (e. g. ,  Kneller and Buckee 2000; Eggenhuisen et al. 2020). Due to this simplification, the primary conclusions from this contribution are thus based only on ADCP velocity measurements, with the sediment concentrations contributing further TABLE 1.
ACKNOWLEDGMENTS We thank the crew members of the Solveig III cruise, as well as all technicians and the scientific team involved in the efforts. CJH is funded by the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement No 721403-ITN SLATE. MJBC was supported by a  Royal Society Research Fellowship  ( DHF/R1/180166 ).
Landscape parameters and water surface area of the Ket River basin The physio-geographical characteristics of the 26 Ket tributaries and the 7 points of the Ket main stem (Table  S1 , Fig. S1 ) were determined by applying available digital elevation model (DEM GMTED2010), soil, vegetation and lithological maps. The landscape parameters were typified using TerraNorte Database of Land Cover of Russia  (Bartalev et al. , 2020; http://terranorte. iki. rssi. ru). This included various type of forest (evergreen, deciduous, needleleaf/broadleaf), grassland, tundra, wetlands, water bodies and riparian zones. The climate parameters the watershed were obtained from CRU grids data (1950-2016)  (Harris et al. , 2014)  and NCSCD data  (Hugelius et al. , 2013; doi:10. 5879/ecds/00000001), respectively, whereas the biomass and soil OC content were obtained from BIOMASAR2  (Santoro et al.
, 2010)  and NCSCD databases. The lithology layer was taken from GIS version of Geological map of the Russian Federation (scale 1 : 5 000 000, http://www. geolkarta. ru/). We quantified river water surface area using the global SDG database with 30 m² resolution  (Pekel et al. , 2016)  including both seasonal and permanent water for the open water period of 2019 and for the multiannual average (reference period  [2000] [2001] [2002] [2003] [2004]. We also used a more recent GRWL Mask Database which incorporates first order wetted streams  (Allen and Pavelsky, 2018).
Data analysis Carbon concentrations and fluxes for all dataset were tested for normality using a Shapiro-Wilk test. In case of the data were not normally distributed, we used non-parametric statistics. Comparisons of GHG parameters in the main stem and tributaries during two sampling seasons were conducted using a nonparametric Mann Whitney test at a significance level of 0. 05. For comparison of unpaired data, a nonparametric H-criterion Kruskal-Wallis test was used to reveal the differences between different study sites. The Pearson rank order correlation coefficient (p < 0. 05) was used to determine the relationship between CO2 concentrations and emission fluxes and main landscape parameters of the Ket River tributaries, as well as other potential drivers such as pH, O2, water temperature, specific conductivity, DOC, DIC, particulate carbon and nitrogen, and total bacterial number.
Carbon emission and lateral export (yield) of the Ket River basin The С emissions (> 99. 5 % CO2, < 0. 5 % CH4) from the lotic waters of the Ket River basin were assessed based on total river water coverage of the Ket watershed in 2019 (856 km², of which 691 km² is seasonal water, according to the Global SDG database). Given that the measurements were performed at the peak of spring flood in 2019, we used the maximal water coverage of the Ket River basin to calculate the emissions during May and June, and baseflow measurements for July-October period. For C emission calculation, we used the mean values of CO2 emissions of the main stem and the tributaries (1. 31±0. 81 g C m -2 d -1 for spring flood; 2. 11±1. 86 g C m -2 d -1 for summer-autumn baseflow) which covers full variability of both tributaries and the Ket River main channel (Table  1 , Figure  3 ).
A significant step toward these goals is the development of a web-based, open-access platform that provides: (1) a reference library of known and unknown biological sound sources (by integrating and expanding existing libraries around the world); (2) a data repository portal for annotated and unannotated audio recordings of single sources and of soundscapes; (3) a training platform for artificial intelligence algorithms for signal detection and classification; and (4) a citizen science-based application for public users. Although individually, these resources are often met on regional and taxa-specific scales, many are not sustained and, collectively, an enduring global database with an integrated platform has not been realized. We discuss the benefits such a program can provide, previous calls for global data-sharing and reference libraries, and the challenges that BACKGROUND Aquatic (i.
, 2020; Duarte et al. , 2021). Researchers now routinely collect substantially more PAM data, on an increasing number of taxa, and in more locations than ever before, from freshwater to marine, from shallow waters to the deep, and from tropical to polar regions  (Wall et al. , 2021). Higher sampling frequencies and longer deployment durations mean that datasets may now easily exceed terabytes in size and years in duration, potentially containing millions of sounds and hundreds of different types  (Waddell et al. , 2021; Wall et al. , 2021). This makes manual classification of underwater sounds by experts-the traditional method of verifying call presence and source identificationincreasingly difficult  (Mooney et al. , 2020; Waddell et al. , 2021). PAM is already used for a multitude of biological applications.
The benefits of a global sound library include: (1) a full inventory of known underwater sound sources; (2) a baseline of unidentified biological sounds; (3) the foundation for a training platform for detection and classification algorithms (at both a source and soundscape level); (4) standardized metadata for understanding how, when, and where the recordings were made; and (  5 ) an open-access (including for citizen science/public users) database to make aquatic biological sounds more accessible to the general public and allow them to upload sounds and add to the dataset (see Figure  2 , for a conceptual diagram of such a potential integrated library). In addition to these benefits, the global sharing of such an expansive database-from potentially numerous contributors-holds the potential for multiple broadscale collaborations on regional and international trends of PAM detections.
Applications of an Inventory of "Known" Sounds Creating a reference library of aquatic sounds from known origins will broaden our reference list for confirming the sources of sounds that appear in recordings and help expand our knowledge of aquatic acoustic diversity, as well as our understanding of taxonomic biodiversity and ecology. Bringing known sounds together in a unified depository or single platform with links to multiple existing databases facilitates easy comparison among species, locations, species repertoires, and recording methodologies. Studies often focus on a single or a limited number of species and, therefore, so does a project's data analysis. Although the advent of multi-species automated detection platforms has brought significant advances in the terrestrial environment  (Potamitis, 2014; Sueur and Farina, 2015; Farina et al.
, 2018; Kahl et al. , 2021) , such analyses are currently lagging behind in the underwater environment. The potential to process each dataset for sources beyond the focal species is rarely undertaken due to the funding and effort required as well as a lack of individual knowledge about all the different sounds and sources that exist. Easing this burden requires a collective effort to detect, identify, characterize, and collate sources. A global reference library of underwater biological sounds would increase the ability for more researchers in more locations to broaden the number of species assessed within their datasets and to identify sounds they personally do not recognize. Such access would ultimately lead to a description and catalog of acoustic biodiversity around the globe, and an increased understanding of acoustic ecology.
Spatiotemporal Species Mapping The expansion of PAM data collection has increased our understanding of spatiotemporal patterns of individual species' presence and acoustic behavior. As a result, reported distributions of these species are being expanded. Even some of the great whales are being found in places they were not expected  (Allen et al. , 2021) , and occasionally a new species  (Rosel et al. , 2021)  or a new sound  (Rice et al. , 2014; Cerchio et al. , 2020)  is discovered. This fact could prove vital for soniferous fauna, as our ever-changing climate ensures that many species are modifying their distributions and broadening or reducing their ranges (e. g. ,  Scheinin et al. , 2011; Ramirez et al. , 2017; Bonebrake et al. , 2018). Biologically important areas can be mapped; spawning grounds, essential fish habitat, and migration pathways can be delineated (e.
Basis for Machine Learning Development A library of reference sounds requires only a handful of examples for each individual sound type. In contrast, a dataset for training artificial intelligence (AI) requires a far larger number of signals, ideally several thousands of examples (e. g. ,  Madhusudhana et al. , 2020, used > 11 ,000 replicates of the same call type to build robust detectors). The library itself can be of benefit as it provides the basis from which the AI datasets can be developed either through providing numerous examples for directly training models, or for facilitating event mining from previously collected data streams  (Xie et al. , 2008; Zhang et al. , 2013). For example,  Miller et al. (2021)  initiated an open-access library of annotated recordings to train and evaluate automated detectors of Antarctic blue whale and fin whale (B.
physalus) calls. The library was designed to include recordings from a broad range of instruments, locations, environmental conditions, and years, to ensure that robust detectors can be developed and tested across a suite of recording conditions. However, given the scope of this library initiated through the Southern Ocean Research Partnership Fin and Blue Whale Acoustics Group  (Van Opzeeland et al. , 2014) , it is unlikely to be extended to other regions and taxa. As the number of samples of a given sound type reaches critical mass, and recordings of them are also sufficiently rich in signals recorded under different conditions (e. g. , SNR, acoustic environment, recording methods), that sound type can be flagged as available for the development of detection algorithms. Indeed, presenting the information on current sample numbers within each sound type could promote contributors to target them, increasing the likelihood of collectively bringing the dataset up to the required level.
Database of Unknown Sounds A database of unidentified sounds is, in some ways, as important as one for known sources; as the field progresses, new unidentified sounds will be collected, and more unidentified sounds can be matched to species. These sounds and the times and locations of their recording can form a basis for future identification and ease mapping of the species' distribution once the source has been confirmed. Given the increasing rate of data collection, it is better to start building a map of these sounds as soon as possible. The library can also provide evidence to help test hypotheses of source species for unknown sounds if there are sufficient recording locations that can be compared to distribution maps of potential source species (e. g. , those produced from catch data or visual census).
Detectors are further applied to wavelets and the cepstral domain as well (e. g. ,  Alias et al. , 2016; Noda et al. , 2016; Malfante et al. , 2018). Machine and deep learning (AI) methods have been increasingly used to classify and detect sources in multiple applications (e. g. ,  Lin et al. , 2017; MacAodha et al. , 2018; Bergler et al. , 2019; Stowell et al. , 2019). There have been considerable advances in the fields of facial and voice recognition that have advanced public use of phonebased apps to identify music, plants, and the calls of frogs and birds  (Kahl et al. , 2021). However, this success has been largely due to the enormity of the respective databases from which AI algorithms can be trained, a goal that has only recently become possible in the aquatic environment and only for selected call types.
The sheer enormity of data now collected in many underwater acoustic studies, together with the myriad of signals often present and the extreme amount of time required to search these records in more "standard" methods, means there is a clear opportunity for AI to improve efficiency and extract more information from these datasets. Increasingly, neural networks and other AI methods are being used to detect marine mammals in historical recordings such as of humpback whales across the Hawaiian archipelago  (Allen et al. , 2021) , and multiple cetacean species along the west coasts of Canada and Australia  (Mellinger and Clark, 2006). Detections of pulse trains, typically based on previously identified or grouped inter-pulse timing (the time between pulses of sound) are being successfully applied to count the number of echolocating individuals and fish sounds within datasets  (Bahoura and Simard, 2010; Le Bot et al.
, 2020; Kahl et al. , 2021; Waddell et al. , 2021)  and even made end-to-end learning feasible, in which AI models automatically learn features from raw audio to perform signal detection and classification. Successful examples include avian calls  (Bravo Sanchez et al. , 2021) , odontocete clicks  (Luo et al. , 2019; Roch et al. , 2021) , and frog calls  (Xie et al. , 2020). Hand-selected features, such as peak frequency and frequency bandwidth measured manually or automatically, are no longer a necessity. Developing a global database that can assist in modifying or developing algorithms in the underwater environment holds significant potential for detecting, classifying, and quantifying spatiotemporal distribution and abundance of aquatic fauna. Such a global database of known and unknown sound sources can benefit both supervised and unsupervised machine learning.
Supervised machine learning is effective when training data of detection/classification targets are available. However, most underwater biodiversity assessments are unable to make sure all sound sources are already covered in the training database. Unsupervised machine learning may help discover the structure of sound categories from a substantial number of unlabeled recordings and reduce the effort required in manually annotating signal types and characteristics  (Frasier et al. , 2017; Phillips et al. , 2018; Lin et al. , 2021; Ozanich et al. , 2021). Deep-learning models that have learned the structure of labeled and unlabeled recordings archived in this global database will be adaptable to other applications, via transfer learning  (Yosinski et al. , 2014). Therefore, this global library will benefit the detection and quantification of signal types, which may be added to a suite of acoustic metrics, to be used collectively as scene classifiers, to routinely characterize the soundscape.
Any AI model must learn the difference between target signals and background noise. Voice recognition techniques often begin with clean recordings and synthesize training data by adding noise to the known reference  (Lu et al. , 2013; Xu et al. , 2015). These are then used to train models to extract speech from recordings that contain real noise and target signals in long-term recordings. Such data augmentation techniques have proven effective in improving the performance of AI models and have been widely applied in speech and music enhancement models (see review in  Lin and Tsao, 2020). Recordings of biological sounds with high SNR will therefore be crucial to the development of a marine fauna AI database. The ultimate goal of an AI algorithm is that it can accurately classify sounds through any or all recordings of any duration and noise level.
An audio database for training AI models requires large numbers of recordings for one species or sound type of unknown origin. The goal of building such a database is to train a model that can effectively recognize species-specific or sound type acoustic features. This requires signals that have been recorded and processed to a certain set of criteria. Although it is difficult to assess how many recordings will be needed, in general the greater the number of sound samples and the higher the sound quality, the more reliable and precise the automatic classification becomes, as the algorithm learns and improves its performance with increasing data availability  (Zhong et al. , 2020). For species with more complex vocal repertoires, greater amounts of training data further improve classification.
Increased sampling efforts from citizen scientists could be invaluable for the detection of vocal fauna in coastal and inland waters. For example, FishBase 7  uses community input to provide information on each species (Froese and Pauly, 2021), while Redmap (Range Extension Database & Mapping project) 8  is more explicit, inviting the general public to spot, log, and map marine species that are uncommon in Australia, or along particular parts of the coast to monitor changes in species distribution. Future online libraries, such as the Open Portal to Underwater Soundscapes (OPUS) 9  would be expected to facilitate public contributions, similar to WhaleFM, 10  a citizen science project that focused on categorization of call types produced by two cetacean species.
g. ,  Ainslie et al. , 2017). Each criterion may not be required for entry of a sample into the library, but the level of information supplied determines the level of potential use of a sample within the database. There are three criteria that determine how useful a recording could be to the database and how it could fit with known information about the species and its soniferous behavior around the world. These relate to the information available about the recording and the source species: This information provides a broader picture of how the local environment may have affected the animal producing the sound. Defining these requirements, their level of detail, and the final platform design requires the collective expertise of biologists (who have experience related to the potential numbers and types of sounds a species may exhibit), acousticians (who appreciate the impact that propagation losses, sampling methods, and processing techniques may have on the characteristics of the audio clips), signal processing experts (who develop and apply detection, classification, and recognition algorithms, and who can detail the needs of turning example signals into a database for automated detection), and data scientists/database developers (who can develop a scalable and searchable database that can be effectively used and accessed by a broader user community).