dataesr/scientific-paragraphs-categorization huggingface dataset:
X and Y shifts, expressed according to West/East and South/North directions, were transformed into X T and Y T considering a coordinate system linked to the local orbit ground track direction. X T axis follows the orientation of the orbit ground track [i. e. , flight path direction relative to the West/East direction assessed by calculating the orientation of the track between the first and last footprint (of a same beam) of each orbit from v2 dataset] and Y T axis is perpendicular to X T , forming a local orthonormal coordinate system, centered on the initial footprint position (v1 or v2). Angular deviations can therefore be estimated when transforming new X T and Y T to polar coordinates, i. e. , the footprints Euclidean distance to the initial position (0;0) and the shift angle relative to the track direction (X T ).
First, shift magnitudes' mean, median, and standard deviations were assessed. Then, mean relative shift distances and directions were used for dataset mean positions intercomparison. The mean positions were also compared by beam, so as to identify possible beam-dependent behavior. Additionally, the temporal evolution of shift distances and directions was visually analyzed by plotting the positions of successive footprints belonging to the same orbit. For visual simplification, the temporal variability was illustrated for three datasets (v1, v2, and v1_1). It was assumed that orbit segments over the study areas can be assimilated to a line, and compared footprint position spread along that line for different datasets. To define the reference track line [Fig. 4(a) ], we used the first and last v2 footprint of each track.
Footprint Euclidean distances to the line were calculated [Fig. 4(b) ] and reported on the final figure [Fig. 4(c) ]. This highlights differences between ground tracks among the different datasets. 2) Elevations and Heights Qualification: GeoGEDI outputs are expected to improve the agreement between GEDI and reference elevations and heights. Therefore, ground elevation and surface height errors were analyzed. Ground elevation errors are expected to diminish as the algorithm is based on minimizing ground elevation errors. However, height errors analysis provides a fully independent evaluation of the algorithm performance. It consists in comparing GEDI RH98 data with DHMref. The evaluation relied on four standard metrics: MAE (2), mean error [ME (3)], error standard deviation [σ (4)], and RMSE (5).
These metrics were computed for the six different datasets (v1, v2, v1_1, v1_4, v2_1, and v2_4) MAE = 1 n n i = 1 |z i -ẑi | = 1 n n i = 1 |dz i | (2) ME = 1 n n i = 1 (z i -ẑi ) = 1 n n i = 1 dz i (3) σ = n i=1 dz i -dz 2 n -1 (4) RMSE= 1 n n i=1 (z i -ẑi ) 2 = 1 n n i=1 dz 2 i = ME 2 +σ 2 (5) where n number of footprints in the dataset; z i DEMref values; ẑi GEDI ground elevations; dz i difference between z i and ẑi ; and dz sample's mean difference between z i and ẑi. The same statistics were used for height estimations, replacing DEMref by DHMref and z by h. For each footprint, available auxiliary information included: 1) the study site, 2) forest vs. nonforest status, 3) shift magnitude, 4) and a local slope indicator. The latter was defined as the ground elevation range at each GEDI footprint level, and was computed from the 1-m raster DEM using v1 footprint positions.
Forest vs. nonforest status was established using both the forest map (see Section II-C-2) and DHMref. All nonforest footprints of the forest map were assigned the "nonforest" class, while forest footprints with a less than 2-m DHMref value were reclassified as "nonforest," in order to remove footprints acquired over clear-cuts or areas that changed from forest to agricultural land between the last forest map update and GEDI data acquisitions. Distributional metrics were compared for several datasets, defined based on auxiliary information. To evaluate the shift magnitude influence, footprints were divided into five classes based on quantiles of shift magnitude distribution, resulting in an equal number of footprints per classes. Classes were noted C Q1 , C Q2 , C Q3 , C Q4 , and C Q5.
87°to 34. 74°. Fig. 8  illustrates GeoGEDI positions' temporal evolution for an orbit segment and highlights differences between ground tracks corresponding to the various datasets. V1 and v2 tracks are nearly parallel, which translates the bias correction announced by NASA. Tracks obtained with GeoGEDI wobble around v2 tracks, and may vary quickly over time, as illustrated in Fig. 8. In Landes, local variations are greater than in Vosges. Within only 3 km, the v1_1 track can deviate by more than 50 m from the reference track line [Fig. 8(c)].
As expected, the higher the slope indicator, the higher the errors in ground elevations [Fig. 10 (a) and (b)]. For example, v1 ground elevation RMSEs were 0. 98, 1. 70, 2. 87, 4. 65, and 9. 05 m for the five slope classes reported in Fig. 10. Moreover, the higher the slope indicator, the greater the improvement brought by GeoGEDI, and, compared to v1, v1_1 ground elevation RMSEs were improved by 9. 7%, 31. 3%, 48. 2%, 59. 2%, and 63. 4%, for classes C 1 , C 2 , C 3 , C 4 , and C 5 , respectively. Similar results were obtained for v2_1 regarding v2, with improvements of 5. 7%, 12. 7%, 21. 4%, 31. 7%, and 41. 7% for all five slope classes. The slope effect on height estimates is illustrated in Fig. 10(c ) and (d). For all datasets, the smaller the slope, the better the estimate. For v1, GeoGEDI outputs improved the flattest footprints' height accuracy by 4.
Despite improved footprint geolocation in GEDI v2, already corrected for the systematic error components, there is room for additional improvement. Yet, its performance depends on the topography, with lack of convergence in very flat areas. The method showed efficient to correct for ISS attitude and altitude variations for a diversity of forest environments, and to assess GEDI data quality with more confidence. The methods' relative simplicity allows for fast and efficient large-scale deployment, wherever a high-resolution DEM is available. With improvements in the range of those obtained with more complex methods based on waveform processing, the method is a good alternative candidate to process GEDI data prior to implementing methods requiring a precise matching of data sources, such as for data fusion purposes.
This work was supported in part by the  SLIM  project, supported by the  TOSCA Continental Surface Program  of the  Centre National d'Etudes Spatiales (CNES)  under Grant  4500066524 ; and in part by the  CNES  for the Postdoctoral position of  Maxime Soma  and the cofounding by  INRAE  and  IGN  for the Ph. D. thesis of  Anouk Schleich. The work of  Cédric Vega  was supported by  Labex ARBRE  under Grant  ANR-11-LABX-0002-01.
Up to now, TESS has detected more than 6000 planet candidates (TESS Objects of Interest, TOIs), including more than 1300 that could be smaller than 4 R ⊕. The exploration of planets larger than Earth and smaller than Neptune is an area of great interest. Since such planets are not present in our Solar System, our understanding of their origins and formation mechanisms is limited. Interestingly, demographic studies performed by  Fulton et al. (2017)  on the basis of California -Kepler Survey exoplanets sample -a subset of transiting planets from Kepler with high-resolution spectroscopic followup of their host stars (CKS:  Petigura et al. 2017; Johnson et    The paper is structured as follows. Section 2 presents the data from TESS and all ground-based observations. Stellar characterization, validation of the transit signals, and transit analyses are presented in Sect.
Ground-based photometry The pixel scale of TESS spacecraft is 21 per pixel  (Ricker et al. 2015). A targeted star might not be alone in a single pixel. Other stars in the same pixel might be suspected to be the source of the TESS detection. Even if the transit signal is on target, the depth might appear shallower because of the contaminating nearby stars. To confirm the signal on target and validate its planetary nature, a series of precise ground-based observations were collected using five observatories as part of the TESS Followup Observing Program (TFOP 1  ). We made use of TESS Transit Finder (TTF) tool, which is a customized version of the Tapir software package (Jensen 2013), to schedule our observations described hereafter.
Stellar activity With an ecliptic latitude of β = +81. 05 deg, TOI-1680 is located near the northern ecliptic pole in the TESS CVZ. Targets in the CVZ are highly valuable for extracting long-period rotation rates. We first visually inspected the TESS PDC-SAP light curves and found no hints of rotational modulation nor evidence of flares. We then used the Systematics-Insensitive Periodogram (TESS-SIP 6  ) to build a periodogram for photometric data from all the 19 sectors. This tool creates a Lomb-Scargle periodogram, while simultaneously detrending TESS systematics using a similar method to that described in  Angus et al. (2016)  for detrending systematics in the NASA Kepler/K2 dataset. Since the rotational period of the star might be removed by the PDC pipeline, TESS-SIP uses the TESS target pixel file (TPF) data, and apertures assigned by the TESS Pipeline to reproduce a simple aperture photometry (SAP) light curves of the target.
We applied this operation on all the 19 observing sectors. Searching for periods between 10 and 365 days, we applied TESS-SIP on the target and on all the background pixels outside of the TESS pipeline aperture in the TESS TPFs for TOI-1680. The SIP powers, presented in Fig. 6 , show a marked similarity between the periodograms of the target and of the background. For comparison, the lower panel shows the ratio of the target to the background powers. We do not see any significant peaks where the target would display power that would be substantially greater than that of the background. We also used TESS-SIP on each sector alone. We did not find any clear stellar rotation signal nor consistency between the periodograms of any sectors. In short, there is no hint of rotational variability detected for TOI-1680 in the TESS dataset, which is consistent with the lack of detectable Hα emission in its optical spectrum.
Transit modeling We jointly analyzed the light curves from TESS and groundbased instrumentation described in Sect. 2 using the EXOFASTv2 (Eastman et al. 2019) software package. We included the TESS photometric data described in Sect. 2. 1 from sectors 14 to 50. We detrended the ground-based light curves for the airmass, as well as for either the background or the half width at half maximum (HWHM) of the PSF. The choice of these parameters was based on the likelihood maximization. Some light curves were detrended only for airmass, especially the partial ones. Table  1  shows the detrending parameters of each light curve. The detrending was done simultaneously with the transit fitting to ensure a good propagation of the uncertainties on the derived parameters. We fixed the eccentricity to zero assuming the orbit to be circular (see justification below).
We set the NOMIST flag to disable the MIST stellar track that constrains the star and, instead, we imposed Gaussian priors on the stellar mass (0. 1800 ± 0. 0044 M ), radius (0. 2100 ± 0. 0064 R ) and temperature (3224 ± 100K) from our determinations reported in Table  3  as well as uniform priors on the period (P = 4. 8026 ± 0. 1P d) and transit epoch (T c ± P/3) from the values reported in ExoFOP. We set the NOCLARET flag to disable the Claret tables  (Claret 2017; Claret & Bloemen 2011)  that are used to fit the quadratic limb-darkening parameters u 1 and u 2 and we applied our own gaussian priors computed using the PyLDTk code  (Parviainen & Aigrain 2015)  for each passband (see Table  4 ). TESS light curves are corrected for contamination, but we still fit for dilution of the transit signal in the TESS band due to the neighboring stars using 0±10% of the contamination ratio reported in the TIC 259168516 on ExoFOP as Gaussian prior as recommended by  Eastman et al.
(2019)  to account for any in the correction. We ran the EXOFASTv2 analysis until convergence when the Gelman-Rubin statistic (GR) and the number of independent chain draws (Tz) were less than 1. 01 and greater than 1000, respectively. To test the impact of the detrending on our derived parameters, we performed independent analyses of the light curves with another code. We used the PyTransit package  (Parviainen 2015)  and linear models of detrending vectors (FWHM, airmass and background), as done in  Wells et al. (2021)  and  Schanche et al. (2022). We found good agreement between the fits and therefore continued with EXOFASTv2 for the full analysis. The detrended and modeled light curves are presented in Fig. 7  and the TESS phase-folded light curve is presented in Fig. 8. The results are reported in Table  5.
Acknowledgements. This research received funding from the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation programme  (grant agreement no. 803193/BEBOP ). B. V. R. thanks the  Heising-Simons Foundation  for support. TRAPPIST is funded by the  Belgian Fund for Scientific Research (Fond National de la Recherche Scientifique, FNRS)  under the grant  FRFC 2. 5. 594. 09. F, with the participation of the  Swiss National Science Fundation (SNF). The  ULiege 's contribution to SPECULOOS has received funding from the  European Research Council  under the  European Union 's  Seventh Framework Programme  ( FP/2007-2013 ) (grant Agreement n •  336480/SPECULOOS ), from the  Balzan Prize and Francqui Foundations , from the  Belgian Scientific Research Foundation (F.
R. S. -FNRS ; grant no. T. 0109. 20 ), from the  University of Liege , and from the  ARC  grant for Concerted Research Actions financed by the  Wallonia-Brussels Federation. This work is based upon observations carried out at the  Observatorio Astronómico Nacional  on the  Sierra de San Pedro Mártir (OAN-SPM) ,  Baja California, México. SAINT-EX observations and team were supported by the  Swiss National Science Foundation  ( PP00P2-163967  and  PP00P2-190080 ), the  Centre for Space and Habitability (CSH) of the University of Bern , the  National Centre for Competence in Research PlanetS , supported by the  Swiss National Science Foundation (SNSF) , and  UNAM   PAPIIT-IG101321. The postdoctoral fellowship of KB is funded by  F. R. S. -FNRS  grant  T. 0109. 20  and by the  Francqui Foundation.
This paper is based on observations made with the MuSCAT3 instrument, developed by the  Astrobiology Center  and under financial supports by  JSPS KAKENHI  ( JP18H05439 ) and  JST PRESTO  ( JPMJPR1775 ), at  Faulkes Telescope North  on Maui, HI, operated by the  Las Cumbres Observatory. This work is partly supported by  JSPS KAKENHI  Grant Number  JP18H05439  and  JST CREST  Grant Number  JPMJCR1761. This work was partially supported by a grant from the  Erasmus+ International Credit Mobility program  (M. Ghachoui). M. G. is F. R. S -FNRS  Research Director. L. D. is an F. R. S. -FNRS  Postdoctoral Researcher. J. d. W. and M. I. T. gratefully acknowledge financial support from the  Heising-Simons Foundation ,  Dr. and  Mrs. Colin Masson  and  Dr. Peter A. Gilman  for Artemis, the first telescope of the SPECULOOS network situated in Tenerife, Spain.
, 2020). Such uncertainties are then propagated in all further network analysis. Considering uncertainty in input parameters thus improves interpretation of ENA results and allows for statistical comparisons with other models. This approach was recently demonstrated in a model of the Irish Sea where generating ranges for ENA indices based on uncertainty in dietary data provided more trustworthy results  (Bentley et al. , 2019a). In this study, we present an updated Ecopath model  (Janjua et al. , 2015)  of the western Baffin Bay coastal and shelf ecosystem that describes ecosystem structure and function using ENA-derived indices and respective uncertainty distributions. We present eight indices that describe the size, growth and development of the system, the cycling and retention of biomass within the system, and the role of the different system components.
Material and methods The development of this model is a joint interdisciplinary and collaborative effort, including: datasets from the Green Edge Project (http://www. greenedgeproject. info), a multidisciplinary oceanographic research project conducted in Baffin Bay in 2016 to understand the dynamics of the phytoplankton bloom in the Arctic Ocean and how it affects lower compartments of the food web, as well as human populations; data on benthos and Arctic cod (Boreogadus saida) from Amundsen Science CCGS Amundsen icebreaker expeditions (https://amundsenscience. com); and updates from a preliminary Ecopath model developed for this region  (Janjua et al. , 2015). In establishing the input parameters for the western Baffin Bay (WBB) ecosystem model, consultations with experts on the ecology of several marine mammal, seabird, and fish species that occur in the area were used to supplement published literature and improve understanding of the ecosystem.
Functional groups The local food web was simplified into 30 single and multi-species functional groups and set to years of 2011-2016 (Table  S1 ). The years represented were chosen based on data availability, especially for zooplankton and primary producers that provide the base of the trophic network. Detailed data for these lower trophic levels were obtained during the Green Edge expedition, where local abundance, production and consumption rates of zooplankton, phytoplankton, and bacteria were determined  (Massicotte et al. , 2020; Saint-Beat et al. , 2020). Due to their importance for Inuit subsistence harvest we included the following species as single-species groups: polar bear (Baffin Bay subpopulation), ringed seal, narwhal (Eastern Baffin Bay stock), bowhead whale (Eastern Canada-West Greenland population), walrus (Odobenus rosmarus rosmarus) (Hudson Bay-Davis Strait stock), and Arctic char.
Aggregating species into functional groups, especially when the data available for such species are scarce, decreases complexity in the model, which tends to also decrease the uncertainty in model predictions  (Geary et al. , 2020). Seals (other than ringed seals) and seabirds that frequent the area in the summer were included in two multi-species groups. We aggregated fish and large invertebrates in seven functional groups (Arctic/polar cods, with polar cod being Arctogadus glacialis, small pelagic fish, sculpins/eelpouts, small demersal fish, large demersal fish, large crustaceans, and cephalopods), and benthic organisms into four functional groups (polychaetes, echinoderms, bivalves, and other benthos). Zooplankton species were also grouped based on their sizes and feeding preferences following Saint-Beat  et al.
, 2020) , and abundances were estimated during the Green Edge expedition in 2015 and 2016  (Massicotte et al. , 2019; Massicotte et al. , 2020; Saint-Beat et al. , 2020)  and converted to biomass using allometric formulas (Text S1, Tables  S7  and S8 ). Abundances of phytoplankton and sea ice algae, bacteria and detritus were also measured during the Green Edge expedition in open water of Baffin Bay, and under the ice in an ice-camp off the west coast of Baffin Island  (Massicotte et al. , 2019; Massicotte et al. , 2020; Saint-Beat et al. , 2020). Biomass data for benthos was included when available; when not available, we set the EE to 0. 95 and let Ecopath estimate it. All biomasses were included as biomass per area (ton km À2 ).
We used the life-history tool in FishBase (www. fishbase. org) to make these calculations for the different fish species within the model  (Froese and Pauley, 2008). Fishing mortality was included for Arctic char from the subsistence harvest, and for Greenland halibut from the commercial fishery. For benthos and other invertebrates, P/B rates are poorly described. Due to lack of data, the P/B ratios for all benthic groups, as well as cephalopods and large crustaceans (shrimps and crabs), were based on Ecopath models representing other high latitude marine regions  (Pedersen and Zeller, 2001; Guenette et al. , 2005; Aydin et al. , 2007; Hoover et al. , 2013b). In these models, P/B ratios were calculated either based on local growth/production studies available for some species or based on Brey's database on microbenthic productivity  (Jarre-Teichmann and Guenette, 1996).
Ecopath diet matrix The diet matrix was constructed based on data from published diet studies of stomach contents, stable isotopes (SI), or fatty acid (FA) composition studies in the region (Table  S4 ). SI and FA composition studies quantify these food web markers in marine species and can provide useful insights regarding their trophic position, foraging habitat and food source when comparing SI and FA composition of predator with their potential prey  (Kelly, 2000; Budge et al. , 2006). We prioritized stomach content studies reporting prey contribution on a weight basis when available because, contrary to reported values on a frequency basis or SI and FA methods, values on a weight basis are more representative of dietary mass flows. When not available in the region, we considered diet studies from other Arctic regions.
Ecological Network Analysis-ENA Ecopath calculates a set of ENA indices, but these are single values derived from a unique flow matrix with no associated uncertainty. In order to account for uncertainty in input parameters, we performed an ENA of the WBB ecosystem using the enaR package (version 3. 0. 0), which takes into account uncertainty in input parameters when deriving ENA indices  (Borrett and Lau, 2014). To do so, the Ecopath model was converted to the SCOR format (Ulanowicz and Kay, 1991), consisting of node storage (corresponding to the biomasses in Ecopath), network inputs in the system (gross primary production and diet imports), network exports out of the system (fisheries and harvest), respiration for each living group (calculated in EwE outputs), and a flow matrix (corresponding to Ecopath consumption matrix), all in tons km À2 year À1.
, 2018). For this analysis, we used the enaUncertainty function in the R software package enaR  (Lau, 2017; Hines et al. , 2018) , which combines linear inverse modeling with Monte Carlo sampling to generate a set of 10,000 balanced network parameterizations based on the original WBB model and uncertainty data for each parameter  (Hines et al. , 2018). Linear inverse modeling is an empirical technique for estimating the best linear stochastic model consistent with a set of multivariate data  (Penland, 1989). In this case, it combines the uncertainty data for each parameter with the original model to generate a set of plausible models. The Monte Carlo sampling technique randomly samples from the distribution of possible values within the defined uncertainty ranges. This set of models was then used to calculate distributions of ENA indicators.
Acknowledgments We acknowledge our partners in Qikiqtarjuaq, the hamlet of Qikiqtarjuaq and hunters and trappers' association, who are active participants in this project. We also thank the valuable contribution of experts consulted during model development:  Nigel Hussey ,  Steven Ferguson ,  Caroline Bouchard ,  Cory Matthews ,  Kyle Elliot ,  Marcus Dyck ,  Marianne Marcoux ,  Courtney Watt-Wheeler ,  Mark Mallory , and  Margaret Treble. We thank the data contribution from the research programs  Green Edge  ( M. Babin ),  ArcticNet  and  Amundsen Science  ( M. Geoffroy ).