dataesr/scientific-paragraphs-categorization huggingface dataset:
In particular, CARDcold is well correlated in the direction (r = 0. 88) and magnitude (1σ ∼ 0. 26) of interannual variability, as well as the overall magnitude of spring GPP (low RMSE and MBE). The range of performances across within-model experiments reveals important processes, as well as uncertainty in process representation, in driving the magnitude and variability in spring GPP. For example, the ORCHIDEE data assimilation experiment (exp3) shows consistently and substantially lower overall correlation (e. g. , r = 0. 59 from 2001-2018) than corresponding free-running experiments (exp1 and exp2, r is 0. 78-0. 82) but has reduced RMSE and MBE (RMSE is 0. 63 g C m -2 d -1 vs. 1-1. 14 g C m -2 d -1 ). Likewise in SiB3, prescribing an empirically based but fixedin-time LAI of 4. 0 m 2 m -2 (exp2) reduces mean bias but degrades variability (r = 0.
In CARDAMOM, the LAI is a prognostic quantity (a function of foliar C and leaf carbon mass per area). In the absence of LAI observational constraints, the CARDAMOM LAI is indirectly informed by the constraints of time-varying GPP on DALEC2 parameters (see Sect. 2. 3). Our results suggest that additional improvements are possible with careful consideration of in situ-measured vegetation parameters. TBM-MIP experiments also offer insight into important environmental controls and process representation. Air temperature is an effective constraint of spring GPP onset (CLM4. 5, ORCHIDEE-exp1, Fig. 7  and Table  2 ), but it can be degraded when large-scale data assimilation does not account for local to regional vegetation characteristics in parameter optimization (e. g. , ORCHIDEE-exp3, Table  2 ).
In the photosynthesis module of OR-CHIDEE, the temperature dependency of parameters are described by Arrhenius or modified Arrhenius functions following  Medlyn et al. (2002)  and  Kattge and Knorr (2007). In general, the functions are used to estimate the potential rates of Rubisco activity and electron transport based on temperature, as these rates are needed to determine photosynthetic capacity  (Medlyn et al. , 2002). The lowest temperatures for productivity mentioned in these studies are 5 and 11 • C, respectively. Additionally, there is a test at the start of the photosynthesis subroutine that prevents the computation of photosynthesis if the mean temperature over the last 20 d falls below -4 • C. For our study, the only ORCHIDEE experiment that uses specific data related to the plant functional type of this site (OCO-2 SIF data for US-NR1) is ORCHIDEE-exp3.
g. ,  Bacour et al. , 2019; Smith et al. , 2020; Bloom et al. , 2020). Joint assimilation of these datasets, coupled with observed meteorological forcing, has the potential to introduce more emergent constraints of vegetation change with respect to environmental change, thus improving overall estimates of productivity. Future work will assess the joint impact of SIF, ET (evapotranspiration), the LAI, and biomass data as effective constraints on light-use and water-use efficiency  (Smith et al. , 2020) , which is expected to improve the ability of CARDAMOM to use light with respect to increasing biomass subject to longer growing seasons and heat and water stress.
Conclusions Despite mechanistic advances in ecosystem modeling, it is still a challenge to simulate temporal variations in GPP. In an attempt to dissect the environmental controls on GPP in an evergreen needleleaf ecosystem, we analyzed the impact of temperature on spring (March-May) productivity by implementing a cold-temperature GPP limitation within a modeldata fusion system (DALEC2-CARDAMOM). The coldweather GPP limitation allows for improved model estimates of mean spring productivity at Niwot Ridge, specifically CARDAMOM's ability to match the interannual variability observed in tower-derived mean spring GPP. Furthermore, CARDAMOM is able to match spring interannual variability between model and tower data outside of the training period. When compared to TBM-MIP models, controls that appear to impact model performance include the inclusion of water stress (e.
Code availability. The CARDAMOM code used in this study is available here: https://doi. org/10. 5281/zenodo. 49041 (Yang et al. ,  2021b)    Data availability. The Ameriflux US-NR1 data were obtained from https://doi. org/10. 17190/AMF/1246088 (Blanken et al. , 2020). The US-NR1 data used in this study, as well as the CARDAMOM and TBM-MIP outputs, are publicly available and provided in. nc file format at http://doi. org/10. 5281/zenodo. 4928097 (Stettz, 2021). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/bg-19-541-2022-supplement. Author contributions. SGS, NCP and AAB designed and performed the research. AJN, BR, CB, FM, IB, YZ, BQ and MS contributed model simulations. DRB, SPB and PDB contributed obser-vational data. All authors contributed to the writing of the paper and/or revision of the manuscript.
The experiment involved the deployment and data collection of a large array of instruments including current profilers, surf-zone drifters, topo-bathymetric surveys and image acquisition from a fixed videocamera station and a camera equipped Unmmaned Aerial Vehicle (UAV). Wave and tide data were continuously retrieved (every 30 and 60 min, respectively) from a 6 km offshore wave buoy and from the Bayonne-Boucau tide gauge, which is located within the mouth of the Adour river, 4 km north of the study site. Figure  2  shows the offshore wave and tide conditions during the field experiment. LPCA beach was exposed to Atlantic W-NW long-period swells, where significant wave heights H s ranged from 0. 4 to 4. 2 m and peak wave periods T p ranged from 5 to 16 s (Figure  2a ). Offshore wave conditions were relatively energetic (average H s of 1.
cBathy Algorithm The cBathy algorithm (Holman et al. [19] ; https://github. com/Coastal-Imaging-Research-Network/cBathy-toolbox [44] , accessed on 20 January 2020) is based on the linear wave dispersion relationship to estimate depth and thus requires a time series of images with the presence of waves in intermediate or shallow water depths. Images are typically sampled at 1 or 2 Hz over a 17 min collection to yield a burst or stack of images. Data runs are typically collected hourly but can be reduced to half-hourly for field experiments or places where conditions, such as tide or morphology, change rapidly. The image spatial resolution should be adequate to resolve the anticipated dominant wavelengths. For the case of LPCA beach, the wavelength for a typical 10 s wave in 1 m depth is ∼31 m.
The following are available online at https://zenodo. org/record/661717 6#. Yp4WQerMLIU, accessed on 6 June 2022, Video S1: LPCA beach morphological evolution during October 2018 field experiment. (a) Time exposure (timex) image consisting of averaged pixel intensity over the record length of the stack and (b) standard deviation image showing the amount of change in pixel intensity over the record length of the stack in order to highlight preferential wave breaking. The magenta line indicates the zero-elevation contour relative to NGF-IGN69. (c) Kalman-filtered cBathy video-derived bathymetries. Elevation contours are spaced at 0. 5 m intervals relative to NGF-IGN69. (d) Bathymetric change maps; difference between cBathy-derived bathymetries (panel c) and the cBathy-derived bathymetry from 8/Oct/2018 07:30:00 GMT, where red colors indicate sand accretion and blue colors sand erosion with respect to 8 October 2018 video-derived bathymetry.
M. ; project administration, B. C. ; funding acquisition, B. C. All authors have read and agreed to the published version of the manuscript. Funding: This research received external funding by CONACyT (México) through the PhD scholarship of I. R. -P. (grant 540839). The LPCA field experiment received financial support from Region Nouvelle Aquitaine (contract number 2017-1R20107) and was carried out as part of the project ME-PELS (contract number 18CP05), performed under the auspices of the DGA and led by SHOM. B. C. and V. M. are funded by Agence Nationale de la Recherche (ANR) grant number ANR-21-CE01-0015. Data Availability Statement: Data presented in this study are available on request from the corresponding author.
Acknowledgments: LPCA beach is a monitoring site labeled by the  Service National d'Observation (SNO) Dynalit  (https://www. dynalit. fr,accessed on 6 June 2022) with additional funding from the  Observatoire de la Côte Nouvelle-Aquitaine (OCNA)  to ensure the maintenance of the video station. The installation of the video station at the top of Biarritz lighthose was allowed by le  Service des Phares et Balises. Storage resources for this study was provided by the computing facilities  MCIA (Mésocentre de Calcul Intensif Aquitain) of Univ. Bordeaux and Univ. Pau et Pays de l'Adour.
Supplementary Materials: The following are available online at https://zenodo. org/record/661717 6#. Yp4WQerMLIU, accessed on 6 June 2022, Video S1: LPCA beach morphological evolution during October 2018 field experiment. (a) Time exposure (timex) image consisting of averaged pixel intensity over the record length of the stack and (b) standard deviation image showing the amount of change in pixel intensity over the record length of the stack in order to highlight preferential wave breaking. The magenta line indicates the zero-elevation contour relative to NGF-IGN69. (c) Kalman-filtered cBathy video-derived bathymetries. Elevation contours are spaced at 0. 5 m intervals relative to NGF-IGN69. (d) Bathymetric change maps; difference between cBathy-derived bathymetries (panel c) and the cBathy-derived bathymetry from 8/Oct/2018 07:30:00 GMT, where red colors indicate sand accretion and blue colors sand erosion with respect to 8 October 2018 video-derived bathymetry.
LiDAR Motors Line finding Control Motor speed Set of models Set of points Figure  1. Overview of the approach: the LiDAR sensor provides points, and, from those points, lines (models) are extracted and from those lines the motor speeds are deduced. The objective is to identify the crop rows from LiDAR data. The left part of the figure depicts a top view of the scene (a robot moving between two crop rows), the middle part depicts the data from the sensors (i. e. , points corresponding to plants) and the right part shows an ideal result from the line finding algorithm (that is, the left and right crop rows have been identified from the LiDARs data set).
Simulation and Results To test the algorithms under several controlled environments, a simulation has been designed based on ROS middle-ware and Gazebo robot simulation. These tools are widely used in the robotics community, and thus in agricultural robotics  [16] [17] [18]. It can be noticed that all the source code of this simulation can be downloaded from GitHub  [7]. This section presents the developed simulation as well as the methodology used to test the algorithms and the results of the conducted tests.
The number of processed points is consistent with what is expected: the first algorithms named Ruby and RG consider the same number of points, while the algorithms RGOP, RGOPPN, and RGOPPNI use fewer points (due to the filter point set Z * ). This can be seen in Table  5. The considered sensors have a rate of 40 Hz (Section 4. 1. 1), thus they provide a data set every 25 ms. According to Table  4 , the RGOP approach needs less than 3 ms to process a data set. That is, it can be used in real time with this sensor, as can all the other algorithms. It must be emphasized that the crops (more precisely the weed positions) were randomly generated (the source code for the generation of the simulated crops is available in Ref. [7] ). This suggests that this navigation approach will provide reliable results in a wide range of crop configurations.
First, human environments are manifold and much harder to characterize than a rodent's cage. Second, collecting cognitive data of large samples from populations living in different environments is very costly  [20]. To overcome these limitations, we measured non-verbal spatial navigation ability in 3. 9 million people across all countries and examined a subset of this data (397,162 people, in 38 countries). We used a cognitive task embedded in a video game, that is predictive of real-world navigation skill  [16, 21, 22]. While the task has been described previously  [16] , we now report new data not previously published. We focused on spatial navigation due to its universal requirement across cultures, and parallels to rodent studies  [23, 24]. We quantified the complexity of participants' environments with OSMnx, a tool giving access to the street network topology of cities anywhere in the world  [25].
Results and discussion We used the Sea Hero Quest database, which contains the spatial navigation behaviour of 3. 9 million participants measured with a mobile video game, 'Sea Hero Quest' (SHQ)  [16, 22]. SHQ involves navigating a boat in search of sea creatures (Figure  1 ). Performance of SHQ has been shown to be predictive of real-world navigation ability  [21]. It has also allowed to differentiate high-risk preclinical Alzheimer's disease cases from low-risk participants  [26]. Here, we focused on the wayfinding task  [16] , where players are initially presented with a map indicating the start location and the location of several checkpoints to find in a set order. To provide a reliable estimate of spatial navigation ability, we examined the data only from participants who had completed a minimum of eleven levels of the game, and who entered all their demographics (see Methods).
Here we now report that participants raised outside cities are more accurate navigators than city-dwellers. Having a tertiary level of education while having grown up in a city is roughly equivalent to having a secondary level of education while having grown up outside cities in terms of wayfinding performance. The sample sizes for each demographic and country are available Table  S1. Given the magnitude of the dataset, most effects are likely to always be 'significant below the 0. 001 threshold'. In the following, we will focus on effect sizes as they are independent of sample size. We computed Hedge's g between the city and not-city groups. To marginalize the effect of age, we computed Hedge's g within 5-years windows, see Extended Data Fig. 1. Averaged over all age groups, g = 0. 13, 95%CI=[0.
The impact of city topology on human spatial ability has previously been theorized in the urban design literature  [33, 34] , but the empirical studies on street networks suffer from limitations, mostly due to data availability, gathering, and processing constraints  [35, 36] , although see  [37]. To overcome these limitations, we coupled our global dataset with the OSMnx toolbox, which provides the street network layout for anywhere in the world via OpenStreetMap  [25, 38]. Street network complexity is a manifold concept, and many metrics have been proposed to quantify it. Shannon's information entropy  [39]  is arguably the simplest and the most general measure of network complexity, from neural to spatial networks  [40] [41] [42] [43]. The entropy of a variable can be interpreted as the average level of uncertainty inherent in its possible outcomes.
To test this hypothesis and directly replicate the environment effect with an independent sample, we designed a city-themed version of SHQ called City Hero Quest (CHQ) and tested participants with it alongside SHQ. In CHQ, the players performed the same task as in SHQ, but driving a car in city streets, see Figure  4e  and Supplementary Video 2. We collected data from new participants on 5 SHQ levels and 5 CHQ levels matched for entropy and difficulty in order to test whether our SHQ results transfer to a city context, see Extended Data Fig. 7b-d. 599 participants were recruited in the United States via the crowdsourcing platform Prolific. We chose to collect data from the US as it was the most represented country in the Prolific participant pool, and a country with a high environment effect size in the initial SHQ dataset.
Sample size justification and full description of this new task are available in the Supplementary Methods. The same data analysis pipeline was applied to Sea Hero Quest and City Hero Quest data. As shown in Figure  4f , the effect size of the environment on the normalized trajectory length is similar with SHQ data (Hedge's g = 0. 27, 95%CI=[0. 06, 0. 47]) and CHQ data (Hedge's g = 0. 34, 95%CI=[0. 14, 0. 54]), positive values indicating an advantage (i. e. shorter trajectories) for participants who grew up in non-city environments. The difference between the CHQ and SHQ environment effect was not significant (see Supplementary Information, CHQ Data Analysis section). This is also consistent with the effect size we found in the initial SHQ dataset when considering participants in the US on the same levels (Hedge's g=0.
60. 8% of the participants provided basic demographics (age, gender, home country) and 27. 6% provided more detailed demographics (home environment, level of education, see Methods). To provide a reliable estimate of spatial navigation ability, we examined the data only from participants who had completed a minimum of eleven levels of the game (including the first 4 wayfinding levels: levels 6, 7, 8 and 11) and who entered all their demographics. We removed participants above 70 years old because we previously showed a strong selection bias in this group causing their performance to be substantially higher than would be expected in unselected participants of the same age  [16]. We also removed participants from countries with fewer than 500 players, or with education or environment levels more than 10-fold imbalanced.
This resulted in 397,162 participants from 38 countries included in our analysis, (see Table  S1 ). Demographic information -Participants were made aware of the purpose of the game within the opening screen. Demographics were provided by consenting participants in two steps. First, their age, gender and home country were asked. Then, after having played a few levels, participants were invited to provide further information such as their level of education and the type of environment they grew up. They were asked whether they were willing to share their data with us and were guided to where they can opt out. The opt out was always available in the settings. Among the included participants there were 212,143 males (mean age: 37. 81 ± 13. 59 years old) and 185,173 females (mean age: 38. 67 ± 14.
g. walking, cycling), relative to passive, car -or public transport-based travel  [51] , resulting from the denser arrangement of local activity locations. We furthermore anticipate that where participants have stated growing up in a city environment that there is a definitive and salient personal association with a city underlying this selection, whereas in other cases (e. g. suburban, mixed) this association is less clear, and better described as non-city. Both factors are common across international settings. Indeed, the observed clear difference between the city group and the other three groups, but little dissociation between the other three groups supports this approach, see Extended Data Fig. 2. To further validate this city / non-city dichotomy, we asked the participants to the City Hero Quest follow-up experiment the street they lived on in their home environment, and computed the entropy of the street network (SNE) in a spatial window around it (see Supplementary Methods).
e. road segment centre points). For each path, the following measures were extracted. Unique streets -Sum of the unique street names provided by Open Street Map encountered along the route. Partitions crossed -Sum of unique partitions encountered during the route. The road network was partitioned using the Louvain community detection algorithm on the dual graph, setting edge cost as angular change. These partitions have been used a proxy for deriving perceived neighborhood boundaries  [58] , and have demonstrated consistency with well-known regions, such as Soho and Mayfair in London, see Extended Data Fig. 10. Snapped angular change -Angular deviations are calculated as the angle of incidence between two adjacent road segments, based on the connecting straight-line segments on each road polyline.
We took the additive inverse of the metric and added an offset, so that W F = 0 corresponds to the worst performances. Pearson's correlation coefficient between WF and the sum of the trajectory lengths of the first two levels (video gaming skills) is weak: r = 0. 10, p < 0. 001, bootstrapped 95%CI = [0. 09, 0. 10]. The implementation of WF is available in the code presented in the Code and Data Availability section. Game Level Entropy -We calculated the Shannon entropy of the Sea Hero Quest level's orientations' distribution similarly as for the cities. To create the equivalent of "streets" in the levels of the game, we computed the Voronoi regions from the game level's layout, and took their edges. The Voronoi region boundaries are considered equivalent to road centre lines in the city context.
Data Availability Due to its considerable size (∼ 1 To), the global dataset is available on a dedicated server: https://shqdata. z6. web. core. windows. net/. A login/password to the server will be provided from the corresponding author upon request. A lighter version with the preprocessed trajectory lengths and demographic information is available at https://osf. io/7nqw6/?view_ only=6af022f2a7064d4d8a7e586913a1f157 We also set up a portal where researchers can invite a targeted group of participants to play Sea Hero Quest and generate data about their spatial navigation capabilities. Those invited to play the game will be sent a unique participant key, generated by the Sea Hero Quest system according to the criteria and requirements of a specific project. https://seaheroquest. alzheimersresearchuk.
Offline Software Tools Every move and action commanded to the positioners is logged in a database that takes a snapshot of the current software state of the robots. This database can be queried offline to analyze positioners for calibration of parameters or positioning performance. Desimeter 46  is a software suite for  processing positioner information offline. This includes making batch queries to the positioner database, processing raw FVC images, and transformations between sky, FVC, focal plane, and local robot coordinate systems. We wrote the desimeter transformation code in isolation from PlateMaker (used online during observation) so that we could cross-check between the two. Positioner calibration parameters (kinematic "arm lengths," central axis location, angular zero-points, effective gear ratios) are calculated by making best fits between commanded angular motions of the motors and measured locations of the fibers.
FVC Centroiding and Spot Matching As described in Section 2. 5, the FVC is used to close the control loop with the positioners. Centroids of spots in the FVC image provide a measure, in pixels, of the relative locations of the fibers and the fiducial sources on the focal surface. The transformation of these measurements from pixels at the FVC image plane to physical coordinates at the focal surface is done by a software module called PlateMaker  (Honscheid et al. 2016) , which is not discussed in detail here. The FVC software is separated into two parts: "Fiberview" and "Spotmatch. " Fiberview records FVC images, detects spots above a fixed brightness threshold with intensity profiles matching the diffraction-limited PSF (this filters cosmic ray hits and hot pixels), and precisely measures pixel centroids of each source.