dataesr/scientific-paragraphs-categorization huggingface dataset:
After the year 2019 we extrapolated the wood harvest data until the year 2020. The HYDE3. 3 population data are also used to extend the wood harvest time series back in time. Other wood harvest inputs (for years prior to 1961) remain the same in LUH2. With the switch from HYDE3. 2 to HYDE3. 3 changes in the land-use forcing compared to the version used in the GCB2020  (Friedlingstein et al. , 2020)  are pronounced. They are thus compared in Fig. 6b  and their relevance for land-use emissions discussed in Sect. 3. 4. 2. DGVMs implement land-use change differently (e. g. an increased cropland fraction in a grid cell can either be at the expense of grassland or shrubs, or forest, the latter resulting in deforestation; land-cover fractions of the non-agricultural land differ between models). Similarly, model-specific assumptions are applied to convert deforested biomass or deforested area, and other forest product pools into carbon, and different choices are made regarding the allocation of rangelands as natural vegetation or pastures.
, 2021) , the approach implemented here adds the DGVM estimates of CO 2 fluxes due to environmental change from countries' managed forest area (part of the S LAND ) to the original E LUC flux. This sum is expected to be conceptually more comparable to LULUCF than simply E LUC. E LUC data are taken from bookkeeping models, in line with the global carbon budget approach. To determine S LAND on managed forest, the following steps were taken: Spatially gridded data of "natural" forest NBP (net biome productivity) (S LAND , i. e. due to environmental change and excluding land-use change fluxes) were obtained with S2 runs from DGVMs up to 2019 from the TRENDY v9 dataset. Results were first masked with the Hansen forest map  (Hansen et al. , 2013) , with a 20 % tree cover and following the FAO definition of forest (isolated pixels with maximum connectivity less than 0.
9 ). These estimates differ in many respects: they use different maps of surface f CO 2 , different atmospheric CO 2 concentrations, wind products and different gas-exchange formulations as specified in Table  A3. We refer to them as f CO 2 -based flux estimates. The measurements underlying the surface f CO 2 maps are from the Surface Ocean CO 2 Atlas version 2021 (SOCATv2021;  Bakker et al. , 2021) , which is an update of version 3  (Bakker et al. , 2016)  and contains quality-controlled data through 2020 (see data attribution Table  A5 ). Each of the estimates uses a different method to then map the SOCAT v2021 data to the global ocean. The methods include a data-driven diagnostic method  (Rödenbeck et al. , 2013;  referred to here as Jena-MLS), three neural network models  (Landschützer et al.
, 2021). OSCAR Gasser et al. (2020) Update to OSCAR3. 1. 2, which provides finer resolution (96 countries and regions). LUH2-GCB2019 input data replaced by LUH2-GCB2021. FRA2015 (Houghton and Nassikas, 2017) still used as a second driving dataset, with emissions from FRA2015 extended to 2020. Constraining based on this year's budget data. Dynamic global vegetation models CABLE-POP Haverd et al. (2018) Changes in parameterization, minor bug fixes. CLASSIC Melton et al. (2020) a Non-structural carbohydrates are now explicitly simulated. CLM5. 0 Lawrence et al. (2019) No change. DLEM Tian et al. (2015) b Updated algorithms for land-use change processes. IBIS Yuan et al. (2014) c Several changes in parameterization; dynamic carbon allocation scheme. ISAM Meiyappan et al. (2015) d ISAM now accounting for vertically resolved soil biogeochem- istry (carbon and nitrogen) module (Shu et al.
to inform ocean f CO 2 -based data products. Platform Regions No. of Principal No. of Platform name measurements investigators datasets type 1 degree North Atlantic, coastal 8652 Gutekunst, S. Ship Allure of the Seas North Atlantic, tropical Atlantic, 19 321 Wanninkhof, R. ; Pierrot, D. Ship coastal Atlantic Explorer North Atlantic 15 665 Bates, N. Ship Atlantic Sail North Atlantic, coastal 25 082 Steinhoff, T. ; Körtzinger, A. Ship Aurora Australis Southern Ocean 14 316 Tilbrook, B. Ship Bjarni Saemundsson Coastal 3269 Benoit-Cattin A. ; Ólafsdóttir, S. Ship R. BlueFin North Pacific, tropical Pacific, 76 505 Alin, S. R. ; Feely, R. A. Ship coastal Cap San Lorenzo Tropical Atlantic, coastal 12 417 Lefèvre, N. Ship Celtic Explorer North Atlantic, coastal 18 617 Cronin, M. Ship Colibri North Atlantic, tropical Atlantic, 13 402 Lefèvre, N.
The CT images were anonymized and the only information collected was the age and sex of the individuals. Additionally, the month at which the scans were performed were used to determine the local average temperature and humidity (The World Bank Group, 2021). We excluded the patients displaying nose and sinus disorders such as nasal fractures, rhinitis, or choanal atresia causing an obstruction of the NA, or sinusitis. We also excluded patients under respiratory assistance or scanned with their mouths open, to ensure a functional nasal breathing. The application of these criteria led to the exclusion of 56% of the data collected. Among the remaining individuals, we selected males (N = 99) and females (N = 96) over the age of 20 and divided them into three age categories based on those commonly used in biological anthropology  (Buikstra & Ubelaker, 1994) : 20-35 years (N = 66), 36-50 years (N = 64), and 51+ years (N = 65) (see Figure  S1  for detailed composition of the selected sample).
| Segmentation of the nasal airway NA surfaces were produced through semi-automatic segmentation and 3D mesh reconstruction in the software Avizo v. 9. 0. 1 (Figure  1 ). First, a threshold-based segmentation was used for filling the air in the negative space delimited by the nasal mucosa. Depending on the acquisition parameters of the original CT scans, the threshold values set on the basis of the histogram were slightly adjusted visually to find the best fit to the segmented structures. As a next step, volumes were inspected and corrections were manually made to include areas that were not initially selected to the final volumes corresponding to the NA. We excluded all ducts connecting the NA with paranasal sinuses. The NA was delimited anteriorly by the piriform aperture and posteriorly by the choanae.
Coronal slice (c) of the nasal airway along the plane figured in (b). the segmentation process was performed by one observer (LM) and an intra-observer test (Figure  S2 ) confirmed the repeatability of the procedure. In order to observe the morphological variation in the NA in real physiological conditions, we did not decongest or average the NA to exclude the potential effect of the nasal cycle, as it has been done in several studies  (Patel et al. , 2015; Yokley, 2009). The labels resulting from the segmentation were used to build a surface mesh by applying the marching cubes algorithm  (Lorensen & Cline, 1987)  ("Generate Surface" module in Avizo v. 9. 0. 1). The number of triangles was set to 75. 000 faces for each individual's NA to reduce computation time while maintaining sufficient morphological accuracy.
These surface models were smoothed using a smoothing algorithm allowing minimal surface shrinkage  (Taubin, 1995)  ("Smooth Surface" module in Avizo v. 9. 0. 1). As a pre-processing step, all surfaces were rigidly aligned in position, orientation, and scale with respect to a reference surface (randomly selected) using an Iterative Closest Point algorithm ("Align Surface" module in Avizo v. 9. 0. 1). We then exported each simplified, scaled and aligned surface in PLY file format and converted them into VTK files on Paraview v. 5. 7. 0.
g. , diet, smoking habits, etc), medical history or air quality are out of our reach. Other environmental factors can also influence NA morphology, such as altitude, which we were unable to observe either. Indeed, the locations from which the data were obtained are coastal or valley environments, which limits the elevation variation between 19 m (Phnom Penh, Cambodia) and 561 m (Santiago, Chile) (OpenStreetMap, 2022). However, keeping in mind all these limitations and the fact that the observed variation has a multiple etiology, we discuss two factors that could partially explain this variation and that are difficult to disentangle from each other: genetic and climatic factors. Genome-wide association studies have found significant association for multiple facial traits including nose-related traits and have identified several genes involved in craniofacial development  (Adhikari et al.
The objective of this study is to evaluate the performances of a semi-empirical approach based on the Bayesian theory to retrieve Green Area Index (GAI) from multiple decametric satellites. It is designed to overcome some limitations in existing Radiative Transfer Model (RTM) inversion methods, including the high dimensionality of the inverse problem, the convergence problem due to possible equifinality, and the dependence of some RTM variables on the crop-specific architecture. The PROSAIL model is first inverted in a calibration step using the Hamiltonian Monte Carlo (HMC) algorithm over a global dataset of ground GAI measurements (for maize, wheat, and rice) and the corresponding reflectance observations from Landsat-8, Sentinel-2, and Quickbird to derive crop-specific distributions of PROSAIL input variables.
These distributions were then used as prior information to predict GAI over an independent set of reflectance observations. Results show that the full Bayesian approach provides close estimates of GAI to ground truth, with respective Root Mean Square Error (RMSE) of 1. 01, 1. 33, and 0. 97 for maize, wheat, and rice (R 2 =0. 67, 0. 76 and 0. 63, respectively). The performances are better than those approaches generally reported using radiative transfer models that are non-crop-specific, like the SNAP algorithm for Sentinel-2, but are slightly behind the purely empirical models based on machine learning. However, the proposed approach provides an explicit insight of the joint distribution of PROSAIL variables that are valid for any satellite platform. This constitutes a major advantage against purely empirical models, as it enables to fully exploit large observational datasets from multiple sensors and generalize to other platforms.
, 2000). Thanks to that, the LUT method greatly speeds up the inversion process  (Pan et al. , 2019; Dorigo et al. , 2007). The ANN-based methods provide another efficient way to invert RTMs, using large datasets simulated with RTMs to train an ANN. This approach combines the physical realism of RTMs and the operational efficiency of empirical algorithms, which proved to be reliable by many authors  (Curnel et al. , 2011; Delloye et al. , 2018; Verger et al. , 2008; Weiss and Baret, 2016). Lately, the Gaussian processes regression (GPR) has also been introduced to learn the RTM-simulated database with kernels, which are projection functions evaluating the similarity between the each spectrum and all training spectra  (Campos-Taberner et al. , 2015; Moreno-Martínez et al. , 2018; Verrelst et al.
It will be applied to the widely known PROS-PECT+SAILH model (or PROSAIL), which has been considered as a reference model for the remote sensing retrieval of GAI  (Jacquemoud et al. , 2009). Our approach assumes a number of PROSAIL variables being crop-specific, and therefore, can be calibrated using a training set from GAI and reflectance observations. The first aim of this study is to retrieve the joint distributions of these crop-specific input variables using a Bayesian Monte-Carlo algorithm. Special attention will be paid to analyse the covariance and compensation effects among the cropspecific input variables. Secondly, those calibrated joint distributions will be applied to predict GAI in two ways: (i) as prior information in a full Bayesian approach to estimate GAI from reflectance, (ii) as inputs to generate the training database for an ANN to predict GAI in an operational context.
Study sites and data acquisition A large dataset of 930 GAI ground measurements was collected for maize, wheat and rice at 25 different sites across the world (Fig. 1 ). It results from the compilation of six different ground GAI datasets, i. e. , (i) the IMAGINES database comprising nine sites across Europe and South America, where GAI were sampled during the 2013-2016 period; (ii) datasets from five experimental stations in the Chinses Ecosystem Research Network (CERN) sampled from 2015 to 2018; (iii) the P2S2 validation database for decametric-resolution crop products measured at five European sites in 2018; (iv) the vegetation observations from the SMAPVEX16-MB field campaign in 2016; (v) in-situ measurements in a Spanish commercial plot and (vi) five rice plots over the northeast of China (Table  1 ).
Geo-referenced observations were taken using destructive or indirect methods  (Jonckheere et al. , 2004)  including LAI2000 (Li-Cor Inc. , US), ceptometer (Decagon Inc. , US) and digital hemispherical photographs (DHP)  (Weiss et al. , 2004). While LAI2000, ceptometer and upward looking DHPs provide estimates of the plant area index (PAI) that includes all vegetation green and non-green parts, all the measurements were taken during the vegetative stage when the senescent fraction is very small, allowing to approximate PAI to GAI. Further, all ground GAI data used in this study refer to the definition of 'true GAI'  (Fang et al. , 2019a). This is the case for destructive and DHP measurements, the latter being processed to account for leaf clumping  (Camacho et al. , 2021; Fang et al. , 2019b).
Ceptometer measurements were taken when the sun zenith angle was higher than 45 • , avoiding directions parallel to the rows which minimizes the clumping effect  (López-Lozano et al. , 2007). The LAI2000 measurements provides estimates of the effective GAI. However, LAI2000 measurements were restricted to wheat crops that show marginal leaf clumping as demonstrated later in Fig. 13. LAI2000 measurements over wheat crops are therefore a close approximation of the true GAI. The spatial sampling strategy for most datasets is designed to represent a sample area to the size of a decametric satellite pixel (i. e. , Sentinel or Landsat constellations). GAI measurements from IMAGINES, P2S2 and Fang's datasets followed a same sampling protocol recommended by the VALERI project (http://w3. avignon. inra.
fr/valeri/). In these datasets, an ESU corresponds roughly to a 30 × 30 m 2 area in a homogeneous part of field with about 30 m far from the field borders. For each ESU, at least two to three sub-samples would be used to capture the possible variability. Sites from the CERN datasets shared a standard protocol for crop structure and function measurements defined by CERN. The sampling field (ESU) corresponds a square area with a size no less than with 40 × 40 m 2 , around which a 2. 5 m-width protecting rows were set to reduce the adjacency effects. Within each field, 4-6 nonadjacent sub-samples in 5 × 5 m 2 will be averaged to capture the variability and mitigate possible sampling errors. In the SMAPVEX campaign, each field covers an area of approximate 800 × 800 m 2. Crop GAI was measured once a week at each sample field, in which fourteen replicates (photos) along the two transects will be conducted with a distance of approximately 5 m between photos.
In the Spanish maize field, Quickbird images at very high spatial resolution (2. 4 m) were acquired for reflectance observation. Correspondingly, GAI measurements were taken at 20 randomly-sampled ESUs with the extent as a 3 m × 3 m square. More detailed description of the methods used to estimate GAI for each dataset are given in the references listed in Table  1. Canopy reflectance was observed from several decametric resolution satellites within few days from the ground measurements. For all the sites except MaizeSpain (Table  1 ), cloud-free Landsat-8 or Sentinel-2A (after 2016) images were used. Landsat-8 is one of the most recently launched  (on February 11, 2013)  satellite in the Landsat Data Continuity Mission, which has a sun-synchronous orbit (at ~705 km altitude) and a 16-day repeat cycle (with an equatorial overpass time at approxi-mately10:30 a.
m. ). Landsat-8 carries the Operational Land Imager (OLI) that has eight spectral bands at 30 m spatial resolution covering the visible, the near infrared (NIR) and the shortwave-infrared (SWIR) spectral regions, and one 15 m spatial resolution panchromatic band. Data are acquired with a 15 • field of view offering an approximately 185 km swath. Here, Landsat-8 Level2 collection of atmospherically corrected surface reflectance was obtained from Google Earth Engine, which has been processed as bottom of atmosphere reflectance using the Land Surface Reflectance Code (LaSRC). Available observations were obtained from images overpassing the ground sites within five days from the date of ground measurement. Cloudy pixels were further excluded by filtering the cloud flags (Bit. 3 and Bit. 5 equals to one) in the "pix-el_qa" band of the Landsat-8 surface reflectance data.
We split the 930 available points (Table  1 ) into 637 points for training and 293 points for validation. The split was designed to keep a balanced representation of the crops and sites across the training and validation datasets. For each crop, instead of randomly splitting individual GAI measurements into assumed proportion, we packaged all measurements for each site as the unit for division, in order to guarantee the full independence between training and validation data. Sites from all the six databases (Table  1 ) were mixed and manually split into the crop-specific training/validation datasets, while keeping a roughly 7:3 ratio of GAI records. In this way, balanced variation in crop cultivars, background conditions and GAI measuring instruments should be represented in both training and validation.
The emulation strategy implemented relies on three components (Fig. 2):  (1) Band selection. We selected 40 representative wavelengths in the range between 400 and 2500 nm. The selection is based on a large dataset of PROSAIL reflectance simulated by randomly sampling the input variables according to the distributions presented in Table  3. The reflectance on several combinations of 40 wavelengths (excluding the atmospheric windows on mediuminfrared) were then used to reconstruct the full spectrum using the Whittaker-Eilers smoother  (Eilers, 2003). The 40-wavelength combination providing the lowest RMSE between the simulated and the reconstructed spectra was finally selected (Fig. 3 ). Specifically, the selected wavelengths distribute consistently with previous findings on reconstructing measured reflectance spectra with 30 wavebands  (Jiang et al.
, 2021). (2) Emulation of PROSAIL. Two ANNs were then trained to simulate the leaf spectral properties and canopy reflectance at the 40 selected wavelengths from PROSPECT and SAILH input variables, respectively. A total of 1,000,000 randomly sampled pairs of input variables and output reflectance was generated as the training (70%) and validation (30%) datasets. The ANN for PROSPECT has two layers, with each layer containing 30 and neuros, respectively; while the ANN for SAILH has three layers with 85, 45, 50 neuros for each layer. When training both ANNs, default hyperparameters of Python's scikit-learn package MLPRegressor were used, with a hyperbolic tangent activation function (tanh), the maximum number of iterations as 6000 and the tolerance for the optimization as 1e-8. (3) Emulation of band convolution.
In this step, sensor-specific ANNs were trained to compute directly the reflectance of the sensor bands from the canopy reflectance simulated by PROSAIL at the selected 40 wavelengths. The training dataset is composed of 20,000 pairs of simulated reflectance at the 40 wavelengths, and the band reflectance aggregated from the full-wavelength reflectances using the sensor-specific spectral response functions. The ANN has a two-layer structure, with each layer containing neuros. Same hyperparameters were used as in the second-step, with the exception of a rectified linear unit activation function (ReLU), and the large-scale bound constrained optimization solver (known as LBFGS) to optimize ANN weights.
HMC is, essentially, an iterative sampling algorithm that permits to explore a multi-dimensional parameter space. For the sake of brevity, more details on the implementation of the proposed Bayesian methodology using HMC are given in the supplementary materials. A Python version of the Bayesian implementation is available in this Github link: https://github. com/solowjw717/HMC_Bayesian. git. The estimation of GAI from observed reflectance using the Bayesian framework proposed here is made in two steps (Fig. 4 ): the Bayesian estimation of crop-specific PROSAIL variables (the calibration step), and the estimation of GAI (the prediction step). In the prediction step, two different alternatives are presented: a full Bayesian one (called the full HMC approach) where GAI is estimated following the Bayesian theory using the results of the calibration step; and the HMC + ANN prediction, where an ANN is trained to predict GAI from reflectance using the results of the calibration step (Fig.
Bayesian GAI prediction from HMC (full HMC) In the prediction step, PROSAIL input variables (with the subscript 'pred') are estimated from the remaining 30% of reflectance observations not used in the calibration step. Here, all the prior probabilities for observation-specific variables of PROSAIL, γ pred , follow a uniform noninformative distribution, except σ i , whose prior is given by Eq. (  7 ). Therefore, the prior probabilities of observation-specific variables can be removed from the general expression given in Eq. (3): p(β, γ, σ|ρ o )∝p(β) ∏ nobs i=1 p(σ i )⋅p ( ρ o,i |β pred , γ pred,i , σ i ) (8) where p(β) = 1 2π n/2 ⃒ ⃒ Σ β,cal ⃒ ⃒ 1/2 e -1 2 (β-μ β,cal ) T Σβ,cal -1 (β-μ β,cal ) (9) and n is the number of crop specific variables. The prior probability of crop-specific variables in the GAI prediction step, p(β), is the PDF of the multivariate normal distribution ℵ(μ β, cal , Σ β, cal ) fitted from the posterior distribution β estimated in the calibration step.
GAI prediction using an ANN trained with PROSAIL variables estimated from the Bayesian calibration (HMC + ANN) The ANN-based inversion of PROSAIL has been one of the most efficient methods to predict GAI from reflectance. To train the ANN, a large dataset of PROSAIL input variables and their respective modelled reflectance are necessary to describe the actual relationship between reflectance and GAI. Generally, this dataset is constructed from distributions of PROSAIL input variables that are taken from the literature or from the expected values for some model parameters like N or h  (Baret et al. , 2007; Li et al. , 2015). Thus, these distributions are often suffering from the scarcity and possible lack of representativeness of the accessible information. Further, no information is available on the codistributions between these variables.
The calibration step of the Bayesian approach provides a priori an optimal distribution of the PROSAIL input variables to train an ANN for GAI prediction. This hybrid approach, named HMC + ANN, benefits from the Bayesian calibration of PROSAIL while replacing the computer demanding Bayesian GAI prediction by a cost-efficient ANN (Fig. 4 ). The   S1 ). To generate the training database of the ANN, we employed the pretrained emulator as a PROSAIL alternative to simulate the spectral reflectance at the selected 40 wavelengths, and then the 40-wavelength reflectance was convolved to the target band characteristics. Although the PROSAIL emulator is not strictly necessary for ANN training as the number of PROSAIL simulations is quite small compared to a Bayesian estimation, we preferred to use it in order to be consistent with the full HMC approach.