dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions and discussion OMIP provides useful datasets for reconstructing sea ice evolution over the past decades. Lin et al. (2021)  have shown that the accuracy of the reconstruction depends on the atmospheric forcing used. This paper attempts to explain why this is so by conducting surface momentum and heat flux analyses. The two atmospheric reanalysis products are different in both dynamical and thermodynamical components for the Arctic and Antarctic, such as the air temperature and winds, which contribute to heat flux and momentum flux differences in the ocean-sea ice models. We studied the dynamic and thermodynamic processes contributing to the ice concentration tendencies and their links with surface heat and momentum fluxes as well as the connections between the simulated ice drift and the ice concentration, ice thickness and wind stress.
Acknowledgements. We are grateful to  Noriaki Kimura  and  Sara Fleury  for providing and introducing the ice drift and ice thickness datasets, respectively. We thank the sea ice observational groups and the climate modeling groups for producing and making available their output. Xia Lin is a F. R. S. -FNRS scientific collaborator. François Massonnet is a F. R. S. -FNRS research fellow. https://doi. org/10. 5194/tc-17-1935-2023 The Cryosphere, 17, 1935Cryosphere, 17, -1965Cryosphere, 17, , 2023.
Data availability. CMIP6 OMIP data are freely available from the Earth System Grid Federation. The download links to the observational references used in this paper are listed in Table  3  of Lin et al. (2021). The Coordinated Ocean-ice Reference Experiments, version 2 interannual data (CORE-II) are available at https:// data1. gfdl. noaa. gov/nomads/forms/core/COREv2. html (last access: 3 April 2023; Large and Yeager, 2009). The updated Japanese 55-year atmospheric reanalysis (JRA55-do) is available at https: //esgf-node. llnl. gov/search/input4mips (last access: 3 April 2023;  Tsujino et al. , 2018).
In our current study the difference in pre and postoperative center of rotation was always less than 3 mm. These results demonstrate that this amount of deviation does not appear to have any impact on minimum 2 years clinical outcomes. Limitations of the current study include that it is a single arm study with data collected retrospectively, and length of follow-up is limited to a minimum of 2 years follow-up. Patient demographics, baseline information, and bone quality evaluation are limited. A strength of the study is that the patient population is representative of the surgeon's practice and included patients with a range of glenoid types, BMIs, and ages. The current study demonstrates no migration of the implant with time with the use of a stemless humeral device with both central and peripheral fixation for total shoulder arthroplasty.
5 V range. The internal ESD circuits of the V DDI /G NDI and analog I/O pads include N-MOS transistors with gate-to-source resistors to reduce the overall gate leakage current. The die samples were packaged in a 64-pin ceramic quad flat package. All the power supply and ground pads were bonded to the package pins with spacing in between them to minimize the effect of mutual inductive coupling. This makes it possible to not only inject more power into the supply pads but also to monitor high-frequency signals  [32]. To test the conducted emission and immunity of the IC, a 13 cm × 13 cm four-layer FR4 PCB was designed according to IEC 61967-4  [33]  and IEC 62132-4  [34]  using Altium Designer. The PCBs were not overloaded with nonlinear components to restrict uncontrolled radio frequency (RF)/EM behavior  [35].
The voltage sensor principle is based on intentional aliasing, operating much below the Nyquist criteria, in order to transpose high-frequency signals down to a few megahertz. The influence of parasitic elements, such as the pad capacitance, package, and bonding, can be excluded at such low frequencies  [39]. Hence, the output signal amplitude is a linear function of the input signal amplitude. The Miller amplifier aspect ratios were optimized according to Jespers and Murmann sizing methodology provided in  [40]  using predetermined SPICE generated lookup tables and explicit MATLAB functions  [41]. As illustrated in Fig. 2 (c), the supply rail V DDI1 of the H-clock tree noise circuit is connected to the analog input of the S&H voltage sensor. When a square signal with a constant frequency is injected into the A in of the H-clock tree, the switching activity of the buffers produces conducted noise, which propagates to V DDI1 or G NDI1.
The nature of the assemblage In the light of the new skeletal findings and the aforementioned evidence, it is not inconceivable that SJAPL includes a large number of casualties of violence, potentially in the form of one or more "war layers" resulting from battles and/or raids where the involvement of males was dominant. Previous demographic modelling concluded that the assemblage was the result of violence-related events (massacres excluded) in combination with attritional mortality  22. Unfortunately, only a limited number of demographic scenarios have been tested so far using the Population & Cemetery Simulation software  54  , and the weight of violent events and of attritional mortality in the formation of the assemblage remains unknown. However, demographic evidence of overrepresentation in age categories less likely to die naturally (namely older children and adolescents) in SJAPL may support an important role of mass mortality events, since those age categories do not usually show a surplus in populations affected by re-occurring small-scale violent events [e.
Analysis of trauma Trauma was assessed with reference to the standards of the field  38, [76] [77] [78]. Macroscopic examination of bone surfaces, aided by the use of low-magnification lenses, was used to closely examine fracture margins  79. Morphological and metric data were recorded. Healed injuries (i. e. , antemortem, those inflicted some time before the death) were identified upon detection of fractures showing signs of bone remodeling. Unhealed injuries (i. e. , perimortem, those that occurred around the time of death and, therefore, do not show bone remodeling) were differentiated from postmortem fractures (i. e. , those occurred after the deposition of the skeleton) through analysis of the following features: (1) outline: linear, depressed or stellate (for crania) and longitudinal, transverse, curved or V-shaped (for the postcranium); (2) angle: right, oblique or mixed; (3) edge: smooth or jagged; and (4) presence or absence of some observations, including: beveling, comminuting, conchoidal scars, adhering flakes, peeling and patination  79.
Acknowledgements This work was funded and supported by the  British Academy  under the  Newton International  Fellowship  NF170854  (T. F-C. ), by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie Grant  Agreement No  790491  (T. F-C. ), and by the  Spanish Ministry of Science and Innovation  under the  Consolidación Investigadora 2022  Grant No  CNS2022-136080  (T. F-C. ). We are grateful to the anonymous reviewers, whose comments have brought about a marked improvement in this paper.
In addition, a few expedient cutting elements were hafted and brought to the site with the personal gear, primarily for use in butchering and maintenance tasks. The results of this study, obtained through systematic low-power and high-power magnifications, place NMO as a unique case study for the understanding of the Late Levantine MP. The mint condition of flint tools enabled identification of well-developed use-wear and hafting traces. The significant number of hafted artifacts attested to in this study, particularly in the shaping of pointed items, indicates a high level of planning and anticipation of activities at NMO. NMO inhabitants possessed hafting expertise, as evidenced by handle making for stone tools used in tasks for which hafting is not requisite. It also validates the interpretative potential of hafting when systematically integrated with use-wear and technological data to better understand past human behavior and subsistence strategies during the Middle Paleolithic.
Acknowledgements Excavation and research at NMO are supported primarily by the  Israel Science Foundation  (Grant No. 645/09 ). In addition, the project is supported by the following granting agencies:  The Leakey Foundation , The  Wenner-Gren Foundation ,  National Geographic  and the  Irene Levi-Sala CARE Archaeological Foundation. Tel-Hai College  supports the NMO excavation project with internal grants and by providing research facilities. Additional funding for this research was also provided by the  Spanish MICINN-FEDER  projects  PID2019-103987 GB-C31  and  PGC2018-093925-B-C32 ,  Generalitat de Catalunya-AGAUR  projects  2017 SGR 836  and  2017 SGR 1040 , and  URV  project  2021PFR-URV-126. J. I. M-V. was beneficiary of a PhD research fellowship  AGAUR/ESF  " ESF, Investing in your future " ( 2021 FI_B2 00208 ).
J. I. M-V, A. O. & M. G. CH. Research is funded by  CERCA programme/ Generalitat de Catalunya. The  Institut Català de Paleoecologia Humana i Evolució social (IPHES-CERCA ) has received financial support from the  Spanish Ministry of Science and Innovation  through the  "María de Maetzu program "  for Units of Excellence  ( CEX2019-000945-M ). A special mention to our colleague  Miquel Guardiola , an experienced knapper at IPHES, who passed away recently, and who made an invaluable contribution with the knapping of all experimental artifacts included in this work. This article is written in his honor. We want to thank also  Drs. Lena Asryan  and  Justin Coppe  for the rewarding and fruitful discussions that helped in the functional interpretation of some of the items. We also express our gratitude to  Prof.
Acknowledgements The authors are grateful to  Kathleen Ingenhoven  for excellent organizational support. B. W. is grateful to the  Diet-Friedrich-Baur-Stiftung , personal fees and non-financial support from  Merck ,  Alexion ,  Novartis ,  Roche ,  Santhera ,  Biogen ,  Heidelberg Engineering , and  Sanofi Genzyme , and non-financial support from the  Guthy-Jackson Charitable Foundation ; he is partially funded by the  German Federal Ministry of Education and Research  [grant numbers  01ZZ1603 [A-D] and  01ZZ1804 [A-H] (DIFUTURE)]. KH reports no conflicts of interest. MH reports no conflicts of interest. IK has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities from  Alexion ,  Almirall ,  Bayer ,  Biogen ,  Hexal ,  Horizon ,  Merck ,  Neuraxpharm ,  Sanofi , and  Roche/Chugai.
Acoustic cameras are increasingly used in monitoring studies of diadromous fish populations, even though analyzing them is time-consuming. In complex in situ contexts, anguilliform fish may be especially difficult to identify automatically using acoustic camera data because the undulation of their body frequently results in fragmented targets. Our study aimed to develop a method based on a succession of computer vision techniques, in order to automatically detect, identify and count anguilliform fish using data from multiple models of acoustic cameras. Indeed, several models of cameras, owning specific technical characteristics, are used to monitor fish populations, causing major differences in the recorded data shapes and resolutions. The method was applied to two large datasets recorded at two distinct monitoring sites with populations of European eels with different length distributions.
The method yielded promising results for large eels, with more than 75% of eels automatically identified successfully using datasets from ARIS and BlueView cameras. However, only 42% of eels shorter than 60 cm were detected, with the best model performances observed for detection ranges of 4-9 m. Although improvements are required to compensate for fish-length limitations, our cross-camera method is promising for automatically detecting and counting large eels in long-term monitoring studies in complex environments.
Introduction Active acoustic methods are widely used to study and monitor fish in marine and freshwater ecosystems  [1, 2]. Acoustic devices emit acoustic waves and record the echoes reflected by Based on the results and recommendations of these studies, our objective was to develop a transferrable and new method to detect anguilliform fish automatically from acoustic camera data recorded in situ during long-term monitoring surveys, by pairing computer-vision techniques with morphological analysis approaches to correct detection problems caused by fragmentation of fish echoes. To evaluate the effectiveness of the method and identify its potential limitations, we applied it to two datasets recorded in different rivers that had different distributions of silver eel length. The first population is mainly composed by female (body length from 50 to 100 cm  [27]  although males dominate the second population  [28].
Datasets The datasets were recorded at two monitoring sites. One site (Mauzac, MZC) was located in the Dordogne River (France) at a 50-m wide inlet canal of hydropower plant (S1 Fig). The second site (Port-La-Nouvelle, PLN) was located in the 50-m wide channel between the Bages-Sigean lagoon (France) and the Mediterranean Sea  [17]. At both sites, cameras were set perpendicular to the flow with the field of view (FOV) parallel to the river bottom (i. e. scanning horizontally across the channel), covering up to 10 meters from the camera at MZC and up to 14. 2 meters at PLN (Table  1 ). The recorded images represented a top view of the water, with the X and Y dimensions corresponding to the direction of fish movement (upstream or downstream) and the detection range, respectively  [5, 29]. European eel populations differ between the two sites, with that at MZC containing a large proportion of large individuals (70-90 cm long, according to local fisheries and acoustic datasets) and that at PLN containing mainly small eels (30-60 cm, according to acoustic datasets).
More information about the monitoring sites, their eel populations and the sonar settings can be found in  [17, 30, 31]. Two distinct models of acoustic cameras were used to record the datasets: ARIS Explorer 1800 (at MZC and PLN) and BlueView M900-2250-130 2D (at MZC). Recording parameters differed between cameras and sites, especially the window length and image resolution (Table  1 ). All data were recorded in high-frequency mode (1. 80 and 2. 25 MHz for ARIS and BlueView, respectively) to maximize video resolution and thus eel detection (Fig  1 ).
Data processing All video datasets were watched in their entirety by experienced operators using ARIS Fish software 2. 6. 3 (ARIS files) or the VLC media player (BlueView files). Each European eel was counted and described by the time that it passed (both MZC and PLN), its detection range (PLN only) and its length, which was measured manually using the most representative frame chosen by the operator (PLN only,  [16] ). These visual counts, usually considered the most reliable way to count fish in acoustic camera data  [32, 33] , were used as the reference count (RC) throughout the study. The data were divided into a development dataset (4 h of ARIS data from MZC, with 24 eel passes), one ARIS evaluation dataset from each site (MZC-ARIS, 759 eel passes; PLN-ARIS, 788 eel passes) and one BlueView evaluation dataset from MZC (MZC-BV, 198 eel passes) (Table  1 ).
Automatic analysis pipeline The following method has been developed using Python programming language and especially the opencv  [34]  and skimage  [35]  packages. Our automatic analysis was decomposed in successive steps that used configuration and decision thresholds to carry out the detection, identification, and count of anguilliform fish. Those thresholds were defined empirically from the development dataset. They are adjustable and were based on four metrics: mean pixel intensity Ĩ of the reference image (i. e. an empty image without any object passing through), pixel resolution r (mm), the frame rate fps (both imported from the recording settings) and the minimum length of the fish studied L eel_min (cm), which was set by the operator before the analysis. Step 1: Conversion to AVI. Each raw file of the datasets was converted from its proprietary format (.
ARIS or. SON) into a standard video format (. AVI) using a homemade program written in Python. Step 2: Detection of the frames of interest. Intervals of frames of interest were then extracted from the videos, i. e. the frames on which moving discontinuities were passing through the camera FOV. The discontinuities correspond to moving objects that disturb the initial background of the frames. Detection of those frames was carried out from echograms (Fig 2 ) based on ratios of singular values (SV,  [36] ) of which detailed calculation is available in S1 File. These echograms were generated by analyzing the video frame-by-frame. The aim was to reduce the method computation time by carrying out the next steps of the analysis only on these targeted frames. They were an alternative to that calculated by the ARIS Fish software, which uses the maximum intensity of echoes  [37].
Our use of SV instead of maximums allows us to focus only on elongated shapes, corresponding to anguilliform ones. Hence, our tests on the development dataset videos highlight that the second and third SV explain the main part of the discontinuities. The third SV explains fewer discontinuities than the second one but focuses on the elongated property of the discontinuities. Analyzing the second and third ratios in a complementary way (Fig 2 ), thus allow us to get the precise intervals of time where elongated discontinuities were passing through and thus to target the corresponding frames. Step 3: Detection of the candidates. Objects corresponding to the discontinuities highlighted at the previous step are extracted from the frames using a combination of image processing filters (Fig  3 ).
Each candidate was first classified in each frame before considering its entire track. A candidate was classified as an anguilliform fish if it met all of the decision rules in at least five frames of its track. As for other decision rules, the minimum of five frames was set up from the development database. It corresponds to the trade-off between allowing very short passage to be considered while having enough information to carry out a reliable classification. It could be modified by the user depending on how restrictive he or she wants the classification to be. All candidates classified as eels were listed in a. CSV output file in the directory that contained the raw dataset and correspond to the automatic counting (AC). Several characteristics  were listed for each predicted anguilliform fish (each line): the video filename, mean time of passage, detection frames, detection ranges, total number of detections and the measured length of one fish per detection frame.
The method was applied to the three evaluation datasets, and its effectiveness and the reliability of its results were assessed by calculating the recall, precision, and F1-score, as well as generating confusion matrices. Recall (Eq 1) highlights the method's ability to identify all eels that passed into the camera FOV. Precision (Eq 2) evaluates the method's ability to distinguish eels from other fish or other objects. The F1-score (Eq 3) summarized recall and precision to assess overall performance of the model. The confusion matrices (Eq 4) determined the TP, FP and FN in the three evaluation datasets. True negatives (i. e. a non-eel object that was predicted correctly) could not be counted, since the operators did not list objects other than eels when reading the videos. Recall ¼ TP TP þ FN ð1Þ Precision ¼ TP TP þ FP ð2Þ F1 score ¼ 2 � Recall � Precision Recall þ Precision ð3Þ Confusion Matrix ¼ � TP FN FP À � ð4Þ Linear regressions were calculated to evaluate the relationship between AC and RC and between the number of TP and RC, at an hourly resolution.
The influence of three factors on the recall, precision and F1-scores were qualitatively studied. The first was the acoustic camera model, for which we analyzed the confusion matrices and metrics for a 73-hour period common to the MZC-ARIS and MZC-BV datasets (RC = 56 and 174, respectively). The second factor was the eel's detection range, based on TP and FN distributions, and the change in recall and precision as detection range increased for PLN-ARIS. The third factor was the eel's measured length, based on the relationship between the recall and eel length for PLN-ARIS.
Performance of the method Confusion matrices showed differences between predicted eel detection and the eels observed by operators for the three evaluation datasets (Table  3 ). MZC datasets had higher F1-scores (72-78%) than the PLN dataset (43%) (Table  4 ). ARIS and BlueView had similar recall (74% and 70%, respectively), but ARIS had higher precision (84%, vs. 74% for BlueView). Regression slopes between RC and AC differed significantly (p < 0. 001) from 1 for all three datasets, but large positive linear association are observed for MZC-ARIS (R 2 = 0. 93) and MZC-BV (R 2 = 0. 95), and weaker for PLN-ARIS (R 2 = 0. 41), with a slope of 0. 47 (Fig 5a  -5c ). Regression slopes between the number of TP and RC also differed significantly (p < 0. 001) from 1 for all three datasets, and the correlations for all datasets were stronger than those between RC and AC but differed more between monitoring sites (Fig 5d  -5f ).
Additionally, small fish swimming near each other (S4b Fig) were sometimes detected as one larger individual (20% and 8% of the FP in MZC-ARIS and PLN-ARIS, respectively). Because tracking errors were not considered errors when identifying species, a modified precision (i. e. identification precision) was calculated that excluded them from the FP. Identification precision on MZC-BV was similar to the precision for the ARIS datasets, but was much larger than the precision for identification and counting for MZC-BV (85% for identification and 74% for identification and counting).
Discussion The method developed to identify anguilliform fish using acoustic camera records performed well compared to those in previous studies  [12, 14]. The method was able to automatically identify 74% of the large European eels that passed into the ARIS sonar field of view (recall) with 84% precision (i. e. 16% FP). For the same species, Bothmann et al. [12]  obtained a higher performance (recall = 91%, precision = 96%) with a DIDSON camera but on a 5m long detection range, i. e. 2-3 times shorter than our study. With better image resolution, the fragmented targets were likely not a serious problem for their dataset. Zang et al. [14]  also obtained a similar good performance (recall = 84%) on a few individuals of American eels, with no other fish species in their dataset. Moreover, both studies  [12, 14]  used data from a single acoustic camera model at a single monitoring site.
Those correlations between the number of eels counted by an operator and the eels automatically detected highlight that a constant percentage of eels is efficiently identified by our method on both datasets on the datasets recorded by both camera models even if slopes are significantly different from the 1:1 line. This information could help future studies determine a correction factor sitedependent for the method's count to reach a similar efficiency than an entire reading by an operator. During their downstream migration, European silver eels display a wide range of body lengths, as well as sexual dimorphism (i. e. males are shorter than females  [31] ). Overall, sex determination in the European eel is partly related to the local environmental conditions they encounter during their freshwater phase, such as eel density, recruitment and watershed characteristics  [43, 44].