dataesr/scientific-paragraphs-categorization huggingface dataset:
Replicability and limitations One of the strengths of the approach proposed in this paper is that it is based on widely and easily accessible information through online portals such as those of NOAA and Copernicus. This could make it possible to expand predictions to other areas of the Mediterranean or, more generally, of European seas and world oceans (considering that initiatives similar to Copernicus exist in other areas of the globe). In addition, future applications of this kind of predictive model could allow for the refinement of predictions in already investigated areas, since machine learning techniques (such as RF) are born precisely to exploit the mass of information (big data) that is progressively accumulated. On the other hand, it will be necessary to supplement direct information, like that from MEDITS but also other forms of data collection such as image-based analysis, on the amount of macro-litter present on the seabed.
However, for that fraction of macro-litter already dispersed in sea bottoms worldwide, mitigation initiatives represent the most realistic and feasible strategy to pursue, still considering the enormous limitations that arise when working at high depths in the marine environment  (Canals et al. , 2021). Our approach could also provide a benchmark for monitoring the effectiveness of numerous directives that are put in place at both national and Table  3  GAM coefficients and main statistics for the relationship between number of objects km -2 and weight of objects (kg km -2 ). The GAM model was fitted on the dataset of 5869 records corresponding to the values of marine litter abundance in 1073 cells (corresponding to the MEDITS sampling sites) monitored over seven years. Asterisks mark significant values (* P-value <0.
Evolution of nucleocytovirus aaRSs To elucidate the evolutionary histories of nucleocytovirus aaRSs, we constructed their phylogenetic trees together with a comprehensive set of cellular sequences from a previous study  [33]. This dataset covers wide taxonomic groups: 64 eukaryotic species from 26 phyla, 142 bacterial species from 54 phyla, and 76 archaeal species from 11 phyla. The eukaryotic sequences encompassed both cytosolic and organellar sequences. We estimated their evolutionary histories based on tree topology and their statistical support: ultra-fast bootstrap (UFB; >95%) and Shimodaira-Hasegawa approximate likelihood ratio test (SH-aLRT; >80%). We further constructed subsection trees for some aaRSs to improve the resolution of tree topologies. We assigned an evolutionary scenario for each nucleocytovirus aaRS clade, as shown in Table  1.
Discussion The origin and evolution of viral aaRSs have been debated for around two decades since the discovery of APMV  [4, 13, 14, 23, 25, 26]. In the present study, we used an expanded set of nucleocytovirus aaRS sequences, including those from metagenomic data, with a large dataset of cellular aaRSs to construct their phylogenetic trees. The resultant trees recovered viral aaRS clades and their relationship to cellular aaRSs with strong statistical support, most of which was missing in previous studies. On the basis of our updated aaRS phylogeny, recent transfer from eukaryotes to viruses was identified in the trees of AlaRS, MetRS, TrpRS, ArgRS, and SerRS (Figs. 3, 4,  and S5 ). In contrast, the phylogenetic trees of GlyRS, IleRS, PheRS, AspRS, and GluRS provided statistically supported scenarios of ancient transfers between proto-eukaryotes and nucleocytoviruses (Figs.
Sequence dataset We constructed non-redundant MAGs from 4,168 nucleocytovirus MAGs constructed in previous studies  [30] [31] [32]. Redundancies among these MAGs were removed as described previously  [42]. Briefly, average nucleotide identity (ANI) between MAGs was calculated by dnadiff 1. 3 from MUMmer 4. 0. 0 beta2  [43] , and a pair of MAGs that showed ANI greater than 98% with their alignment covering over 25% of the smaller one were clustered together. The largest MAG constructed in Tara Oceans Project  [32]  was selected as the representative of each cluster. If a cluster contained no MAGs constructed in the Tara Oceans Project, the largest MAG was selected as representative. In addition, a dataset of 224 reference genomes was obtained from the Global Ocean Eukaryotic Viral database  [32].
Identification of aaRSs Genes whose best hit was annotated as an aaRS were considered as putative nucleocytovirus aaRSs. Non-functional genes and possible contamination of aaRSs from cellular organisms were excluded from the dataset by using phylogenetic trees constructed for each aaRS of nucleocytoviruses and cellular organisms collected from a previous study  [33]. Non-functional genes were manually identified as short sequences and long branch sequences of nucleocytovirus. Decontamination of sequences derived from cellular organisms was performed as follows. We first detected nucleocytovirus aaRSs that were not included in clades that comprise multiple nucleocytovirus aaRSs in each tree. If contigs that encode the detected aaRS sequence were from a MAG and contained no nucleocytovirus core gene or gene whose best hit taxonomy was nucleocytovirus, we removed the aaRSs as contamination from cellular organisms.
Phylogenetic analysis Nucleocytovirus eight core genes were identified in the dataset by considering the DIAMOND BLASTP hits that were registered in NCVOG  [45]. Multiple sequence alignments were performed by MAFFT v7. 487  [46]  with the E-INS-i algorithm, which is suitable for the alignment of sequences in which the number of domains is unknown. Subsequently, the sites with gaps in more than 75% of aligned sequences were removed by using trimAl v1. 4. rev15  [47]. Phylogenetic trees were constructed within the ML framework by IQ-TREE 2. 1. 3  [48]. The amino acid substitution model was selected by Model Finder  [49]. Bootstrap values were computed by the SH-aLRT method  [50]  and the Ultrafast bootstrap (UFB) method  [51]. Clades with >80% SH-aLRT and >95% UFB were considered to be statistically supported.
All phylogenetic trees were visualized by iTOL v6  [52]. The origin of nucleocytovirus aaRS was inferred using phylogenetic trees constructed as described above. We ignored the clades comprising fewer than three nucleocytovirus sequences, to reduce the contamination from nucleocytovirus MAGs contaminated by contigs of cellular organisms. We also constructed phylogenetic trees from subsections of data used for tree reconstruction above. These subsections contain a nucleocytovirus clade and its neighboring cellular branches. In addition to SH-aLRT and UFB, transfer bootstrap expectation (TBE)  [53]  was calculated. While conventional bootstrap approaches count exactly the same branches in bootstrap trees, TBE takes into account similar branches by using transfer distances  [53]. The use of TBE support facilitates inferences about the presence of unstable taxa (single taxa that tend to move in and out of clades) and is considered to be particularly useful for deep branches and large datasets.
Acknowledgments We thank the  Tara Oceans consortium  and the people and sponsors who supported Tara Oceans. Tara Oceans (including both the Tara Oceans and Tara Oceans Polar Circle expeditions) would not exist without the leadership of the  Tara Expeditions Foundation  and the continuous support of 23 institutes (https://oceans. taraexpeditions. org). This article is contribution number XXX of Tara Oceans. We also thank Edanz (https://jp. edanz. com/ac) for editing a draft of this manuscript. Computation time was provided by the  SuperComputer System, Institute for Chemical Research, Kyoto University.
Sequence of perching and hunting strike events throughout a Barn owl foraging trip. A) GPS tracks during a typical barn owl foraging trip where perching events (squares) and both unsuccessful (circles) and successful (triangles) hunting strikes are shown. Black arrows indicate flight direction. Successful hunting strikes were identified by the presence of self-feeding events or by the direct return to the nest box (using the acceleration data, ground-truthed with the nest box camera footage). B) The heave acceleration and the estimated force during a perching event (highlighted in orange) and a hunting strike (highlighted in purple). C) Variation in peak landing force for perching events (orange dots, n = 56,874) and hunting strikes (dark dots, n = 27,981). White dots show the estimated mean, and data distribution is represented by both violin and box plots.
The biggest difference in landing force was seen between landing on perches and hunting strikes. Strike forces in our study are the highest recorded in any bird, relative to body mass, with maximal force reaching more than 34 times the bodyweight (100N). This exceeds those previously reported for captive barn owls (41 ), and the kicking strike of the secretary bird (Sagittarius serpentarius) that reached an average of 5. 1 times body weight (42 ). Unlike secretary birds, whose kicking strength depends solely on the muscular power of their lower limbs, owls use the dynamics of their entire body in flight. While this minimizes the chances of prey escape, it is also associated with a potential risk of injury  (34 ,43 ). Our results will be underestimates of the true peak forces, as acceleration was recorded at 50 Hz (data on force development in controlled car crashes are typically recorded at > 2 kHz).
Study area and tag deployment Data were collected from wild barn owls breeding in nest boxes across the Western Swiss plateau, a region characterized by open and largely agricultural landscape (45 ). Over 380 nest boxes were checked for barn owl clutches between March and August in 2019 and 2020, following  Frey and colleagues' protocol (46 ). During the two breeding seasons, 163 breeding barn owls (84 females and 79 males) were equipped with data-loggers (92 in 2019, 71 in 2020, Fig. S1 ). Adult owls were captured at their nest sites using automatic sliding traps approximatively 25 days after the first egg hatched. AXY-Trek loggers (Technosmart, Italy) were attached as backpacks (Fig. 1A  ) using a Spectra ribbon harness (Bally Ribbon Mills, USA). These units include a GPS, set to record animal location at 1 Hz, 30 minutes before sunset until 30 minutes after sunrise, to get the full nightly activity period.
The loggers also include a tri-axial accelerometer, which recorded acceleration continuously at 50 Hz (recording range ± 16g, 10-bit resolution). Each device weighed on average 12. 3 g, representing less than 5% of bird body mass (47 ). Loggers were recovered after approximatively 15 days, with data recorded for 5 nights on average (± 1 night). Owls were weighed at both visits and the averaged body mass from the two measurements was used for later analysis. In parallel to each logger deployment, motion sensitive camera traps (Reconyx HC500 hyperfire) were positioned at the entrance of all nest boxes to document when animals returned to the nest with prey (Fig. 1A  ). Wind data were collected using portable weather stations (Vantage Vue, Davis Instruments corp. ) mounted 2. 0 m from the ground (standard anemometer measurement height) within 100 m of each nest.
Behavioral classification We used Boolean-based algorithms (48 ) to classify flight, landing, hunting strikes, and selffeeding from the onboard acceleration and GPS data (see below). Behaviors were summarized in one-second intervals and linked to the closest GPS location in time. Flight, hunting, and selffeeding behaviors were ground-truthed using video footage of 2 captive barn owls equipped with the same data loggers. Further validations were undertaken for hunting behavior (detailed below). Behavioral classifications used the raw acceleration data, the vectorial dynamic body acceleration (VeDBA) (a summary metric of body motion) and body pitch angle (Fig. S2 , Table  S1 ). VeDBA was derived by smoothing the raw acceleration data over 0. 5 s (the period of two complete wingbeat cycles), to estimate the static/ gravitational component, subtracting this from the raw acceleration in each of the three acceleration channels (49 ), and calculating the vectorial sum from the resulting "dynamic" components.
Pitch angle was derived using the arcsine of the static acceleration in the heave axis (50 ,51 ) and smoothed over 1 second. Flights were identifiable from the acceleration data as periods of take-off, travelling and landing (Fig. S2A ). Take-offs were characterized by a switch from a standing to a horizontal posture (Δ pitch angle > -10 °) and high-amplitude VeDBA (> 1 g) (41 ). Travelling flight was associated with smoothed VeDBA values > 0. 1g, and body pitch values < 30 °. Finally, landings were identifiable as changes from low to high pitch angles (Δ pitch angle > 10 °) and a typical final spike in all three acceleration axes (VeDBA > 1 g). Periods that did not correspond to flight were categorized as stationary behavior. Landings were further classified as either perching events, where owls landed on a perch prior to a hunting attempt or hunting strikes/prey capture attempts (Fig.
Prey provisioning were identified from variations in the sway, corresponding to the owl walking inside the nest box (Fig. S2C ). Both start and end phases of the nest box visits were characterized by a rapid change in the pitch angle (enter: Δ pitch angle < -1. 5 °; exit: < 0. 5 °) along with an increase in the heave and VeDBA values (enter: Δ VeDBA > 0. 5 g; exit: Δ VeDBA < -0. 9), as owls leapt in/out of the nest box. Successful provisioning hunts were further confirmed using nest box camera data when available and, in all cases, by manually checking that the GPS data matched the nest site to identify cases where the owls returned with a single prey for their offspring. Unsuccessful strikes were therefore inferred from identified hunting strikes that were not followed by a provisioning to the nest and/or self-feeding event (Fig.
Data processing Data from the onboard accelerometers can be used to estimate landing force during perching and hunting strikes (Fig. 1B  ), as force is equal to the product of mass and acceleration. To estimate landing force, we extracted the peak vertical component of the ground reaction force in Newtons (N) for every landing event, taking the maximum value of the vectorial sum of the raw acceleration (in units of gravitational acceleration, g), multiplying this by the body mass of the bird (in kg) (52 ,53 ). Hunting strikes were categorized according to whether owls hunted on the wing or from a perch to assess factors affecting the landing force of perching events involved in the sit-and-wait strategy. We therefore considered that owls were using the sit-and-wait strategy if they flew for a maximum of 1 second before the strike (corresponding to c.
a. 6. 5 m from the last perch). Hunting on the wing was defined as cases when birds flew for at least 5 seconds prior to the strike (c. a. 81. 7 m from the last perch). Hunting strikes that did not fit into either category (8% of all hunting strikes) were excluded from the dataset. Finally, perch type was estimated by extracting the median location of each perching event. The habitat within 2 m was then classified according to the main perch type available: trees, roadsides and pasture poles (hereafter referenced as "poles"), and buildings, and assigned as the perch type for each perching event. Habitat categories (roads, settlements, single trees, forest) were provided by the Swiss TLM3d catalogue (Swiss Topographic Landscape Model, resolution 1-3 m depending on the habitat feature) and land use data were provided by the "Direction générale de l'agriculture, de la viticulture et des affaires vétérinaires (DGAV)" and the "Direction des institutions, de l'agriculture et des forêts (DIAF)", for states of Vaud and Fribourg respectively.
The model included bird ID as a random intercept to account for repeated measurements of the same individual over multiple nights, and night ID (nested in bird ID) to account for repeated measurement of the same individual within the same night. The same random effect structure was applied to all the following LMs and GLMs as they were fitted to dataset of similar grouping structure. We next fitted a LMM of the landing force during hunting strikes (logtransformed). Fixed factors in the model included hunting success (a two-level factor: successful and unsuccessful), the hunting strategy (a two-level factor: perching and flying) and their interaction. Sex was also included as a fixed factor. To test whether landing force during perching influenced the success of the next hunting strike, we performed a generalized linear mixed-effect model (GLMM) with hunting success as a binary response variable.
Periods of significant change were identified as those time points where the simultaneous confidence interval on the first derivative does not include zero. All statistical analyses were conducted with R 4. 0. 5 (R Core Team, Vienna, Austria), with RStudio (RStudio Team, 2020) as graphic user interface. LMMs and GLMMs were fitted with the functions lmer and glmer, respectively, implemented in the package 'lme4' (R package v1. 1-27. 1) (57 ) and we used the package 'lmerTest' (R package v3. 1-3) (58 ) to estimate p-values. GAMM model was fit using the gam function from the package 'mgvc'  (R package v1. 8-34) (59 -61 ). For all models, linear predictors were centered and scaled to mean zero and units of standard deviation (i. e. , z-scores) to ensure comparability among variables. We performed pairwise comparisons using the emmeans function from the package 'emmeans' (R package v1.
Reviewer #1 (Public Review): In this paper, Schalcher et al. examined how barn owls' landing force affects their hunting success during two hunting strategies: strike hunting and sit-and-wait hunting. They tracked tens of barn owls that raised their nestlings in nest boxes and utilized high-resolution GPS and acceleration loggers to monitor their movements. In addition, camcorders were placed near their nest boxes and used to record the prey they brought to the nest, thus measuring their foraging success. This study generated a unique dataset and provided new insights into the foraging behavior of barn owls. The researchers discovered that the landing force during hunting strikes was significantly higher compared to the sit-and-wait strategy. Additionally, they found a positive relationship between landing force and foraging success during hunting strikes, whereas, during the sit-and-wait strategy, there was a negative relationship between the two.
While this statement is reasonable, it should be presented as a hypothesis, as no direct evidence has been provided here. However, since information about nestling survival is typically monitored when studying behavior during the breeding period, the authors' knowledge of the effect of acoustic camouflage on owls' fitness can probably be provided. Furthermore, it will be interesting to further examine the foraging strategies used by different individuals during foraging, the joint foraging success of both males and females within each nest box, and the link between landing force and foraging success if the data are available. However, even without this additional analysis on survival, this paper provides an unprecedented dataset and the first measurement of landing force during hunting in the wild.
Acknowledgements This study was supported by the  Swiss National Science Foundation  (grants no. 31003A_173178 ). We thank  A. P. Machado ;  L. Ançay ,  N. Külling ,  A-C. Heinz ,  M. Froehly ,  M. Calvani ,  N. Sironi ,  L. Legrand ,  D. Zurkinden ,  R. Allemand  and  L. Hulaas  for their help in collecting field data;  P. Potier  and " les Aigles de l'Urga"  for their help in behaviour calibration with captive barn owls;  R. P. Wilson  and  W. Allen  for their expertise and assistance throughout all aspects of our study and for their help in writing the manuscript;  L. Willenegger  for providing barn owls drawings and  J. Bierer  for barn owl picture.
The solar forcing prepared for the 6 th round of the Coupled Model Intercomparison Project (CMIP6) has been used extensively in climate model experiments and has been tested in various intercomparison studies. Recently, an International Space Science Institute (ISSI) Working Group has been established to revisit the solar forcing recommendations, based on the lessons learned from CMIP6, and to assess new data-sets that have become available, in order to define a roadmap for building a revised and extended historical solar forcing data-set for the upcoming 7 th round of CMIP. This paper identifies needs for improvements and outlines a strategy to address them in the planned new solar forcing dataset. Proposed major changes include the adoption of the new TSIS-1 solar reference spectrum for solar spectral irradiance and an improved description of top of the atmosphere energetic electron fluxes, as well as their reconstruction back to 1850 by means of geomagnetic proxy data.
105 Other aspects to be considered for CMIP7 include the consistency of the recommended solar forcing with that to be used for paleoclimatic reconstructions in PMIP5. At the time of writing the latter are not yet known. The solar forcing for PMIP4 was based on SATIRE  (Jungclaus et al. , 2017) , which was therefore not fully consistent with the one used for for CMIP6. Another issue is the production of the SSI/TSI dataset for future scenarios. As for CMIP6, we are planning to provide an ensemble of forcing scenarios with daily values up to 2300. These will be produced from one single solar input (the sunspot 110 number or the group number) similarly to the way historical reconstructions of the SSI/TSI are made before solar images or magnetograms became available. For internal consistency it would be preferable to use the same solar proxies, and the same version in both models.
, 2022). This would account for the previously missing contribution of SEPs to stratospheric chemistry in the vertical range of ⇠20-40 km. Ionization rates associated with the lower-energy part of the energetic particle spectrum can still be calculated as in M17 using the analytical approach by Jackman et al. 1980). This approach, however, cannot be applied to high-energy (>100 MeV) protons which initiate the atmospheric nucleonic cascade and can penetrate to the lower atmosphere. For that, we propose an approach based on the ionization   (Usoskin and Kovaltsov, 2006; Usoskin et al. , 2010 Usoskin et al. , , 2011;; Väisänen et al. , 2023). The long-term dataset covering the historical period from 1850 to 1962 can be reconstructed in the same stochastic manner as in M17.
Further, the CMIP6 ozone forcing data-set lacked a realistic representation of polar EPP-induced ozone impacts. This dataset was produced as a weighted composite of two CCMs, whereby one CCM (with a stronger weight in the upper stratosphere and mesosphere) did not consider EPP, while the other CCM underestimated the EPP-induced NO y perturbation in the polar stratosphere  (Szel ąg et al. , 2022) , which resulted in an underestimate of the polar ozone loss and subsequent feedback on 220 temperature and dynamics. This study suggested that part of this discrepancy in NO y was due to an underestimation of EEP-NO x from the MEE forcing data-set. As discussed above, the MEE forcing for the CMIP7 EPP-NO x may be 2-10 times larger (see section 3. 2). This will significantly increase the impact of particle precipitation on stratospheric ozone, at least in the upper stratosphere, in agreement with observational estimates (e.