dataesr/scientific-paragraphs-categorization huggingface dataset:
Logistic fitted curve Seed M. sin × sin Rh. M × g Seed M. sac × sin Seed M. sac × sin potential a Rh. M. sac × sin r 2 correlation 0. 98 0. 98 0. 93 0. 99 0. 98 RMSE 12. 61 12. 98 26. 87 2. 94 13. 89 Mean difference -0. 04 -0. 02 -0. 03 0. 003 -0. 02 Relative error -5. 42 -3. 06 -5. 46 0. 01 -3. 46 Maximum error 0. 22 0. 31 0. 40 0. 05 0. 26 No. of values 44 44 44 12 44 Abbreviations: M × g: Miscanthus × giganteus; M. sac × sin: Miscanthus sacchariflorus × sinensis; M. sin × sin: Miscanthus sinensis × sinensis; Rh. : rhizome; RMSE: root mean square error. a Potential = data from best performing crop at Piacenza only. 17571707, 2023, 4, Downloaded from https://onlinelibrary. wiley. com/doi/10. 1111/gcbb. 13029 by Welsh Assembly Government, Wiley Online Library on [13/03/2023]. See the Terms and Conditions (https://onlinelibrary.
The Sample Dossier contains all observations from the STOP list, along with relevant rover data (e. g. , temperatures, rover location, rover arm position and actions, etc. ). Uploaded to the NASA Planetary Data System (PDS) with a regular cadence, the Sample Dossier primarily consists of instrument-specific and engineering data products. These data are independently delivered to the PDS, and thus the dossier acts as a "one stop shop" for sample-specific results. The Initial Report is a description of each sample in a standardized narrative format, written by the Science Team. This initial report is written within weeks of sample acquisition to capture the reasoning for sampling and to describe the interpretations available at the time of sampling and at the completion of the STOP list. The Initial Report should be thought of as a set of field notes associated with each sample.
Determining the history and nature of the dynamo is important for several reasons. First, a strong dynamo may have prevented atmospheric loss, such that its decline may have played a central role in the transition from warmer and wetter conditions, that is, possibly more habitable conditions in the Noachian, eventually reaching the cold and dry conditions of today. Second, the dynamo history reflects the thermal evolution of the planetary interior including the crystallization of the core and mantle convection. Finally, the direction of the magnetic field as recorded by rocks can be used to test the hypothesis that Mars experienced plate tectonics and true polar wander, and to determine whether its dipole component underwent reversals. As such, establishing the history of the intensity and direction of the magnetic field through combined geochronology and paleomagnetic datasets using oriented rock cores from the Jezero Crater is a key goal of future returned sample studies.
Acknowledgments The authors thank editor  Bradley Thomson  and reviewers  Nicole E. Moore ,  Jade Star Lackey , and  Allan Treiman  for their many constructive comments. We are grateful to the entire Mars 2020 team. The Mars 2020 mission and  Return Sample Science Participating Scientist Program  are supported by  NASA   Mars Exploration Program. K. H. -L. acknowledges a  UK Space Agency   Aurora Research Fellowship  (Grant  ST/.
Data Availability Statement This contribution includes a variety of geospatially located data, including context far-field and workspace images (Mastcam-Z, Figures  3 4 5 6 7 ; Figures S2-S10 in Supporting Information S1 color anaglyphs, Figure  S11  in Supporting Information S1 context mapping, NASA PDS https://doi. org/10. 17189/q3ts-c749), subsurface radar data (RIMFAX, Figures  3 4 5 6 7 , NASA PDS https://doi. org/10. 17189/1522644), rock target close up images (WATSON, Figures 8, 9, 12, and 13; Table  S1  in Supporting Information S1, NASA PDS https://doi. org/10. 17189/1522643), sample core images (CacheCam, Figure  2 , NASA PDS https://doi. org/10. 17189/q3ts-c749), major element compositional laser ionization breakdown spectroscopy (SuperCam-LIBS, Figure  11 , Tables  S2, S3 , S4, S5, and S6, NASA PDS https://doi.
Therefore, it could be possible that the tissue of residence provides cues for an ILCP or LTIP to differentiate toward a specific ILC lineage. ACK N O WLE D G E M ENTS Many thanks to Yann Kerdiles for his help with the manuscript, discussions and in particular for generating the figures. The work was supported by the FRM Amorçage jeunes (AJE20150633331), ANR ACHN (ANR-16-ACHN-0011), ANR PRCI (ANR-17-CE13-0029-01), A*midex Chaire d'excellence to SAvdP and institutional grants to the CIML from INSERM, CNRS and Aix-Marseille University. Figures were generated using biore nder. com.
K E Y W O R D S animal health, bio-logging, body density, buoyancy, marine mammal, real-time monitoring, satellite transmission F I G U R E 1 Animals collect resources to improve body condition and produce viable offspring. Northern elephant seals migrate from haulout locations over vast areas of the North Pacific to forage upon mesopelagic fish (e. g. myctophid). As migration progresses, seals improve their body condition by increasing body lipid stores, which makes them more positively buoyant and is a vital energy store for self-survival and successful reproduction. Top figures are replicated with permission from previous studies  (Adachi et al. , 2021; Costa et al. , 2012; Naito et al. , 2013). Bottom left figure is drift rate data obtained in this study. Bottom right photo of a pup and a female northern elephant seal (with BD-SRDL on the head) was taken by Luke Keehan.
, 2001)  but with additional hardware memory and entirely new onboard data sampling and processing systems to sample both depth and three-axis acceleration. In addition to the parameters available on the standard SMRU-IG SRDLs, these data loggers were designed to calculate and telemeter body density (as an index of lipid-store body condition) via the ARGOS satellite system in near real time, as well as to archive raw data to be recovered when the seals returned to shore. First, an onboard algorithm was implemented to calculate the body density of elephant seals following Equation (2), using analysis of glides during the descent and ascent phases. Because the tag had to be placed on the head of seals to enable satellite telemetry, it was necessary to calculate a correction factor for animal pitch as measured on the head versus back of elephant seals.
U drift = √ √ √ √ √ 1 -sw seal mg 0. 5C d,s A s sw , (2) a = - C d,f A f m 0. 5 sw U 2 + sw seal -1 gsin , (e. g. In addition, surrounding seawater density was estimated based on each glide's depth, using upon analysis of CTD (conductivity, temperature and depth) casts previously made in the geographical area where the seals foraged. In total, 24 elephant seals (with over 1200 data days) were used to develop, test and validate the body density algorithm. Seven freeranging northern elephant seals were tagged with prototype versions of the BD-SRDL running the algorithm onboard for real-time monitoring of the body density changes. Nine free-ranging southern elephant seals were tagged with high-rate archival DTAGs  (Johnson & Tyack, 2003) , obtaining the entire high sampling rate data records with which both drift rates and body density could be calculated.
| Onboard body density algorithm To estimate body density in real time, an onboard algorithm to solve Equation (2) for seal was implemented on the BD-SRDL described above. The body density algorithm is implemented on the onboard microcontroller to derive a seal value for each glide based on tag measurements using high sampling rate depth and three-axis acceleration data (Figure  2 ). The same procedure was used for the simulation experiment emulating the onboard algorithm on archived data. 40 Hz depth and 128 Hz 3-axis acceleration data were processed onboard to derive 5 Hz depth, swimming effort and pitch estimates and hence to identify short passive glide segments (i. e. 5-second sub glide; hereafter, referred to as '5-s-glide'). The choice of a duration of 5 s was based on its effectiveness in previous studies  (Aoki et al.
| Data processing steps of the onboard algorithm Depth and three-axis acceleration were measured with tags attached on the head of seals (Figure  S1 ). Data were processed onboard (Figure  2b ; also see Supporting Information for details of onboard processing procedures) and analysed using a simulator for archived datasets as follows: 1. Acceleration filtering Three-axis accelerations (longitudinal surge, lateral sway and dorsoventral heave acceleration) were filtered to separate accelerations into two components: static (lower frequency) acceleration related to the posture of the seal relative to gravity (e. g. pitch angle) and dynamic (higher frequency) acceleration, primarily created by flipper strokes to generate thrust. Here, static accelerations were obtained using a low-pass filter of 0.
Calculation of body pitch angle from head measurements Diving seals tend to adopt a consistent posture while swimming to maintain a body shape that minimizes drag. Because the BD-SRDLs need to be mounted on the heads of animals to maximize the number of successful transmissions, we established the relationship of the pitch measured on the head-mounted device to the pitch of the long axis of the animals' body, using identical loggers mounted on the head and body of female southern elephant seals. Archival acceleration data were obtained from free-ranging adult female southern elephant seals (dataset 1; n = 5) and were used for head-to-back pitch angle correction (Table  1 ). This fieldwork was conducted at Kerguelen Islands (49°20′S, 70°20′E) during the breeding season. It covered the 2-month post-breeding foraging migration (October-January) from 2010 to 2013.
Each seal was chemically immobilized using a standard protocol  (Mcmahon et al. , 2000). Tags were attached, and body mass and morphometric measurements were collected just before seals departing to sea at the end of the breeding season. Upon return for the annual moult after their post-breeding foraging migrations, seals were again chemically immobilized to recover the data loggers. The five seals were each equipped with two archival tags, one on the head (MK10-X; Wildlife Computers) and another on the back (TDR10-Daily Diary tag; Wildlife Computers) placed in a consistent position on the animal using a quick-setting epoxy glue (Araldite AW 2101). The tags had a depth (pressure) sensor (sampled at 1 Hz) and a three-axis accelerometer (sampled at 16 Hz). The head-back pitch correction factor used three-axis acceleration data from head-attached and back-attached tags on the same individuals as detailed above.
(2004)  where dive was defined as deeper than 10 m depth as per  Adachi et al. (2021) ). We extracted 70 million synchronized data points from four seals. These data were randomly subsampled to 10% of their original to conduct a simple linear regression of body-mounted pitch angle against head-mounted pitch angle using the biglm function in the biglm package distributed via r  (Lumley, 2013) , confirming that the inclination of +24° gives us a good estimates of animal body pitch angle from head-attached tags (r 2 = 0. 97; Figure  S2B ).
Detecting 5-s-glides Each 5-s-glide segment was detected in real time using the following steps. First, any 2-s time window meeting the following two criteria were detected: (i) depth >100 m to minimize the (3) = atan ⎛ ⎜ ⎜ ⎜ ⎜ ⎝ Surge static � � Sway 2 static + Heave 2 static � ⎞ ⎟ ⎟ ⎟ ⎟ ⎠ , TA B L E 1 Dataset summary of 24 female elephant seals (Mirounga spp. ), listed in chronological order of years tagged. a Prolonged glide was not quantified due to translocating experiment (dataset 3) or paucity of archival acceleration data (datasets 4 and 5). b Archival data are not available. c Shallower pitch threshold was applied for dataset 4. d Pitch threshold, glide statistics, drift dive statistics and field measurements were irrelevant and not applied for the usage of the dataset 1. effect of lung gas volume on total animal buoyancy and (ii) root mean square of sway dynamic acceleration <0.
05G to remove any stroking phase. When (i, ii) are met, a consecutive 5-s window meeting the following three criteria were detected: (iii) depth >100 m (same as criterion i), (iv) root mean square of sway dynamic acceleration <0. 05G (same as criterion ii) and (v) absolute pitch angle |θ| > 50 (or 30) degrees for calculating speed (U = △Depth/sinθ). The remaining glide parameters in Equation (2) were quantified as follows: a during 5-s-glides was calculated as the slope of a linear regression of swim speeds against time within the 5-s window; U and θ are the averaged values of swim speed and body pitch angle, respectively, during the 5-s-glides. A value for seawater density ρ sw for each glide was specified based on typical seawater density at each glide depth. We obtained seawater temperature and salinity data along depth through the World Ocean Database (https://www.
nodc. noaa. gov/OC5/SELEC T/dbsea rch/dbsea rch. html)  (Boyer et al. , 2018). From the database, we obtained all available profiling float data during 2015-2020 in the northeast Pacific Ocean for northern elephant seals (longitudes of -180° to -120° and latitudes of 30°-60° without the region of Bering Sea; 657 casts in total) and in the Southern Ocean for southern elephant seals (longitudes of 20°-140° and latitudes of -70° to -40°; 1341 casts in total), which corresponds to where our study species potentially migrated and dived as per previous studies that conduct animal tracking  (Bailleul et al. , 2007; Guinet et al. , 2014; Robinson et al. , 2012). Then, we calculated seawater density ρ sw versus depth (as in  Aoki et al. , 2011)  and fitted a power curve of density against depth for all data points from the northeast Pacific Ocean and the Southern Ocean, respectively (Figure  S3 ).
Satellite transmission Glide parameters (a, ρ sw , U and θ) and ρ seal were transmitted as frequently as allowed by the ARGOS system when seals were at the surface. See Supporting Information and  Fedak et al. (2001)  for the technical description of transmission scheduling. This change in pitch threshold setting was done after detailed effects of the pitch angle were investigated using dataset 2, which included high-sampling raw depth and three-axis acceleration archival data from free-ranging seals (Table  1 ).
| High-sampling archival data using DTAGs Archival data obtained from free-ranging adult female southern elephant seals (dataset 2; n = 9) were used for validation of the onboard algorithm and estimation of drag coefficients (C d,f in Equation 2 and C d,s in Equation 1) (Table  1 ). Fieldwork was conducted at Kerguelen Islands during the elephant seal breeding seasons from 2017 to 2019, to examine the 2-month post-breeding foraging migrations  (Goulet et al. , 2019 (Goulet et al. , , 2020)) , with the same standard protocol as dataset 1. The nine seals were equipped with high-sampling archival accelerometers (DTAG, SMRU-IG) on the head. Similar to the BD-SRDLs, these DTAGs have a depth (pressure) sensor (downsampled from 25 Hz to 5 Hz) and a tri-axis accelerometer (downsampled from 200 Hz to 25 Hz).
| Desktop version of the algorithm for body density estimation To validate the performance of the onboard algorithm, we built a desktop version of the body density algorithm. This version utilized the most detailed and accurate method of detecting glides based on  Aoki et al. (2011). This validation was conducted using the DTAG dataset from southern elephant seals (dataset 2; Table  1 ). We analysed time-series data per day per individual a step-wise process described below. Except for Step 1, each step is comparable to our onboard algorithm described above. The desktop analyses are more fine scale because they determine daily-and individualspecific configurations for the filtering acceleration, rather than using the fixed values across days and individuals (see Step 1 of the onboard algorithm).
| Quantification of drift rate to validate body density in elephant seals To evaluate the empirical correlations between drift rate and body density, we calculated drift rate for datasets 2 through 5 (Table  1 ). First, we visually inspected time-series data of depth and accelerations during drift dives in translocated seals (dataset 3). We wrote custom-written codes for the automated calculation of drift rates based on the visual inspection and previous studies, as detailed below.
Drift rates from translocation seals with body density manipulation Since the number of drift dives is limited in dataset 3 due to the short duration of the trials (Table  1 ), we manually detected drift dives and calculated drift rates using four criteria based on previous studies: (i) a drift dive has the distinctive 'drift dive' profile  (Crocker et al. , 1997) , in which the shallowest depth of a drifting phase is (ii) deeper than 100 m, to minimize the effect of lung gas volume on buoyancy  (Adachi et al. , 2021) , (iii) without any strokes  (Adachi et al. , 2014) , and (iv) where seals are in a belly-up orientation (i. e. heave static acceleration is nearly -1G)  (Mitani et al. , 2010). In total, 25 drift dives were obtained from the three seals (Table  1 ). As detailed below, the visually confirmed criteria were implemented into the custom-written codes for the automated calculation of drift rates in other datasets.
Drift rate determined from free-ranging seals with DTAGs Based on the visual inspection of translocation seals above, we developed custom-written code, updating the method by  Adachi et al. (2021)  as follows: A drift phase must (i) have depths >100 m, (ii) be longer than 20% of the total duration of the drift dive, (iii) have little variance in depth change rate during the entire drift phase (i. e. mean squared residual should be less than 3 m 2 ), (iv) include no strokes, and (v) have heave static acceleration <0 (to choose sideways to belly-up body orientations observed during drifting). We applied this method to dataset 2, in which archival three-axis acceleration data were available to apply the above criteria (iv) and (v). In total, 914 drift dives were obtained from the nine seals in dataset 2 (Table  1 ).
, 2014 (Adachi et al. , , 2021;; Beltran et al. , 2021; Biuw et al. , 2003 Biuw et al. , , 2007;; Richard et al. , 2014; Robinson et al. , 2010 Robinson et al. , , 2012)). Then, the best-fit C d,s was determined per individual of the datasets 2, 4 and 5 (Figure  3b ) by the following four steps: 1. Compile a list of all days that include drift dives: list all days including at least one drift dive and calculate empirical daily The median of best-fit drifting drag coefficient C d,s was 0. 12 (with an individual variation of 0. 09-0. 23) (Figure  3b ), which is substantially lower than the previously reported value (0. 69) in newly weaned pups with a mean body mass of 92. 5 kg  (Biuw et al. , 2003). The 0. 12 value was then used to relate drift rates to body density. averaged.
| Validation of body density algorithm versus drift rates in free-ranging seals Field tests of two prototypes of BD-SRDLs were conducted in 2020 (n = 2; dataset 4) and 2021 (n = 5; dataset 5) in free-ranging northern elephant seals using the onboard algorithm in the tags to calculate body density values and transmit them via satellite, while concurrently having the tags store raw data for subsequent post-processing. The tags were recovered when the animals returned to shore. The stored data were post-processed to calculate body density and drift rate. A simulated version of the onboard algorithms was also applied to data obtained from recorders on southern elephant seals (dataset 2). The hardware of the two prototypes was identical. Still, the onboard algorithm was modified to increase the pitch threshold from 30° to 50° in Prototype-2 (2021; see Table  1 ) to reduce variability in body density estimates.
The BD-SRDLs detected and stored more than 100 5-s-glides per day. About ten 5-s-glides (11. 1 ± 5. reported in the previous study  (Robinson et al. , 2010). This transition was also reflected in increased glides occurring in the ascent phases of dives. The proportion of 5-s-glides during the ascent phase increased significantly as migration progressed and body density decreased (Figure  4d ; Figure  S8 ). This pattern in the proportion of descent/ascent 5-s-glides held for both transmitted and archived data (Figure  S9 ). This was also the case when we simulated the onboard algorithm on archival data on free-ranging southern elephant seals (n = 9; dataset 2), where we detected 239,056 5-s-glides in total (Table  1 ). Body density values decreased as migration progressed, consistent with the expected accumulation of lipid during foraging (Figures Overall, our onboard algorithm detected body density changes in 16 free-ranging seals as expected with respect to measured and simulated drift rate values (Figure  5b ).
| Investigation on source of errors in body density estimation Our body density estimation has variations in values (e. g. error bars in Figure  5a ), and field tests of BD-SRDL implies that pitch threshold is key to reducing errors in body density estimation (Figure  S6 ). To quantify the contribution of a higher pitch threshold to minimize estimation errors relative to other potential parameters, we conducted a further investigation using dataset 2 (n = 9, having high-sampling raw depth and three-axis acceleration archival data from freeranging seals; Table  1 ) (Figure  6 ). Here, four potential parameters were selected as in a previous study  (Aoki et al. , 2011) ; (i) pitch angle, (ii) pitch change during the 5-s-glide, (iii) swim speed and (iv) MSE (mean square error) in a linear correlation with swim speed against time to derive the acceleration a, where larger MSE indicates weaker correlation (i.
e. including more noise in the data). With this set of parameters, we conducted a sensitivity analysis to see how each parameter affected the accuracy of body density estimation as follows; (i) time-series data of measured body density values per 5-s-glide (with pitch angle > |30| degree) per seal was processed using the gam function in the mgcv package distributed via r  (Wood, 2001)  to fit a generalized additive model (GAM) with body density as a response variable and time as an explanatory variable. We obtained a smoothed curve of body density changes over time (Figure  6b ). (ii) The measured body density values were subtracted from the fitted values to calculate the residuals for each 5-s-glide. (iii) The residuals of the 5-s-glides were squared (Figure  6b ). The result shows that body density errors (represented as squared residuals here) were smaller for steeper pitch angles, smaller pitch changes, lower swim speeds and smaller MSEs (Figure  6c -f).
| DISCUSS ION Our devices obtained empirical data that generated verified estimates of body condition of deep-diving seals. Furthermore, the tags relayed these data via the CLS Argos system in near real time. Transmitted body density values were validated against both archival body density and drift rate values in northern elephant seals (Figure  5 ). While body density estimates from seals at sea have previously been demonstrated in elephant seals, the temporal and spatial resolution of such estimates have been limited because of the limited occurrence of drift dives. However, because short glides during dives are relatively frequent during foraging, the methodology developed here improves the temporal and spatial resolution to detect changes in body composition. It can also be used in other species that rarely or never exhibit drift dives.