dataesr/scientific-paragraphs-categorization huggingface dataset:
Partially sampled point clouds per plot We computed the area covered by each constituent point cloud by fitting a two-dimensional hull to the points projected onto a horizontal plane. Then, an area threshold was used to drop any point cloud that covered<90 % of the total plot area (Fig. 2 ), resulting in a final dataset set of 93, 110, 144 and 149 point clouds for riparian (29 plots), coniferous (31 plots), broadleaf (42 plots) and mixed plots (45 plots), respectively. Fig. 3  shows the number of flight lines per plot and class for different forest types. The 'expansion' of the dataset made it possible to pick any one or a combination of point clouds per plot. In other words, it was possible to recreate datasets with different scanning configurations. With multiple possibilities for each plot, there were thousands of unique combinations in which the point clouds could be picked under different experimental scenarios (explained in the following sections).
We only picked the flight lines (or combinations of flight lines) for scenarios A, B, C, AB, AC and BC in these plots. The weighting step was adapted accordingly for those special cases within general scenarios based on homogeneity in scanning configurations. For the remaining plots, i. e. , plots that were not among the common plots, weightings ensured that all available classes (or class combinations) were picked roughly equally. An identical process was followed for the other three forest types. All the possible experimental scenarios can be listed as fl1, A, B, C, fl2, AB, AC, BC, and fl3. Based on the sampling framework described above, we created 5000 unique lidar datasets for each scenario. 2. 3. 3. 4. Scenario with conventional point cloud subsets. We also built models in the conventional way, which is the standard procedure followed when building models with ABA approaches.
Model validation and performance assessment Leave-one-out cross-validation is most suitable for smaller datasets  (Picard and Cook, 1984). Thus, it was employed in this study to analyse the predictive capabilities of the models. A model is built for all but one of the plots at a given instance. The model is used to predict a value for the disregarded plot. This process is repeated until predictions are made for each of the plots. Log-transformation of variables introduces a systematic bias. The bias was corrected using a correction factor as per  Sprugel (1983). The standard error of estimate (SEE) of regression was computed, as given by: SEE = ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ ̅ ∑ (logy i -logŷ i ) 2 (n -p) √ (5) y i and ŷi are the i th observed and predicted values, respectively, in logscale, n is the number of observations, and p is the number of parameters in the model.
Impact of scan angle on models of different scanning scenarios The percentage changes in the means and standard deviations of goodness-of-fit criteria for models built using lidar data for the same plots, based on different scanning configurations, reflect the variability of ABA models and the resulting predictions. The standard deviations (spread) of all three goodness-of-fit criteria (R 2 , rRMSE and MPE) for all the forests decreased when the dataset was comprised of multiple flight lines per plot. However, the distribution means were different according to forest types, and there was no definite pattern. For example, for the riparian plots at Ciron, the distribution means appeared to be broadly stable for all scenarios except for scenario C (20 • <=MSA < 30 • ), which decreased considerably.
We observed that the variations among scenarios A, B, and C tended to be higher than those among scenarios AB, AC, and BC. For the coniferous plots, the means for scenarios fl2 and fl3 were considerably lower than those for scenario fl1 by almost 30 % in each case. We observed the same phenomenon for broadleaf plots, but the differences were lower (fl2-fl1: -0. 6 %, fl3-fl1: -2. 2 %*). Van Lier et al. (2021)  also observed marginally higher errors for models built with multiple flight line data. On the other hand, we observed an opposite trend for riparian plots. Let us consider the goodness-of-fit distribution as unexpected 'risk' when using a dataset acquired with a particular configuration to build ABA models. The risk of having highly variable predictions is reduced when at least two flight lines are used to scan each plot.
Future studies need to assess the prediction variability when ABA models, based on a specific scanning configuration dataset, are used to generate wall-to-wall predictions. The metrics commonly used in ABA approaches are certainly affected by scan angle, as demonstrated by several studies  (Holmgren et al. , 2003; Liu et al. , 2018; Montaghi, 2013). To our knowledge, few studies have explored the effect of the inclusion of metrics sensitive to scan angle in ABA models  (Holmgren et al. , 2003; van Lier et al. , 2021). Van Lier et al. (2021)  recently investigated the effect of scan angle on both lidar metrics and ABA models to predict different forest attributes. In their study, which addresses scan angle effects on area-based models, they found relatively stable goodness-of-fit (R 2 , rRMSE and MPE), with minor differences between models built with and applied to datasets acquired from near-nadir, off-nadir as well as combinations of flight lines.
Moreover, it is interesting to note the better performances with point clouds acquired from single flight lines. For the Bauges site, scenario C (20 • <=MSA < 30 • ) does not have the same negative impact as on the riparian plots, which could be related to the differences in data acquisition properties. MSA classes for both sites are not fully comparable due to the differences in flying altitudes (250 m over Ciron sites and 1050 m over Bauges sites). In addition, depending on the slope of the plots, the lidar pulse-canopy interactions may be different for flight lines with similar mean scan angles but with different azimuths, as illustrated in Fig. 9. In a related study, we found that the inclusion of terrain parameters positively impacted the forest attribute predictions  (Lahssini et al.
Nonetheless, the improvements with voxel metrics were notable. In the Bauges plots, the high slopes of many plots could have affected the interaction of the lidar with the canopy. Due to limited datasets, the azimuth of the lidar acquisition was not considered. In some plots, the tree sizes heterogeneity was very high, with many instances of large trees found near the periphery of the plots. Many such trees were found beyond the plot while a significant part of the canopy was still present in the plot subsets, thereby occluding acquisition from flight lines with certain azimuths. The occlusion was especially prominent for inclined scanning.
Acknowledgements This study was carried out in the framework of the  PROTEST  project, supported by the  French Agency for Ecological Transition (ADEME)  (grant  1703C0069 ,  GRAINE program ) and the  FRISBEE  project, supported by the  TOSCA Continental Surface program  of the  Centre National d'Etudes Spatiales (CNES)  (order N • 4500070632). It also.
In this work, we show that satellite measurements of HDO/ H 2 O ratio of water vapor reflects the spatial, seasonal, and interannual variability (IAV) of ET-P over the Amazon, as opposed to either flux alone, mitigating uncertainty related to taking the difference of two very large fluxes. These isotopic measurements are derived from thermal infrared radiances measured by the Atmospheric Infrared Sounder (AIRS) instrument. The AIRS measurements span 2002 through the present and are accurately calibrated over this time period relative to ground and aircraft data  29  , making them suitable for quantifying the HDO/H 2 O ratio of tropospheric vapor from seasonal to decadal time scales. We calibrate the isotopic data to ET-P at river-basin scales using satellite gravity-based TWS and river discharge (herein TWS/discharge) measurements ("Methods"), and quantify their spatial biases and precision using a global climate model enabled with water isotopes.
iCAM could also affect the scatter; these include variations in cloud microphysical processes and changes in the depth of convection resulting in variations in moisture flux convergence (Fig. 1 ). All these processes are discussed in the "Methods" and are shown to have a negligible impact on the precision of the deuterium-based ET-P estimates from AIRS. The scatter shown in Fig. 2b  represents the uncertainty on the seasonal variability of the δD_004-based ET-P estimates. The overall accuracy is bounded by the TWS/discharge measurements as discussed next. Accuracy of ET-P Estimates. We estimate the accuracy of the ET-P estimates based on the AIRS δD_004 data as the RMS error in the fit between the monthly based TWS/discharge versus AIRS δD_004 estimates of ET-P and TWS/discharge suggested ET-P.
One approach uses TWS retrievals from the GRACE and river discharge measurements over the Amazon by following the equation: ET À P ¼ À ΔW Δt À R; ð1Þ where ET is evapotranspiration, P is precipitation, ΔW Δt is TWS change with time, and R is runoff. Equation 1 is derived from the water balance equation, in which ΔW Δt is estimated by subtracting ET and R from P  10. Here, we use three GRACE TWS retrievals from the Center for Space Research (CSR), the GeoforschungsZentrum Potsdam (GFZ), and the Jet Propulsion Laboratory (JPL), and calculate the arithmetic mean of the three GRACE TWS retrievals  39  to obtain ΔW Δt. During 2003-2015, there are in total 12 one-month gaps and 3 two-month gaps for GRACE TWS. We use spline interpolation to fill the gaps. The river discharge data for each river basin (Fig.
S5 ). In other words, the highest correlation coefficient in Fig. S5  is not in the selected ~2°× 2°region, but in the regions north of 16°S and 61°W, likely because of the transport of air from the wet tropics into the dry tropics. The correlations become negative in the Northern Hemisphere (Figs. S3  and S5 ). Simulations from iCAM suggest the same conclusion, even though there are spatial pattern differences of δD_004, ET, and precipitation between iCAM and observations. We will investigate in a subsequent manuscript if the use of deuterium in the boundary layer better represents nearby variations in ET-P in the dry tropics, which we might expect as previous studies suggest a strong relationship of continental recycling with near-surface deuterium content of water vapor  21. Based on this test, we suggest caution in interpreting variations in δD_004 with respect to ET-P in the mountain regions or in the dry tropics.
The different precipitation products are discussed in the previous section of "Methods". It is noted that the ET obtained from Priestley-Taylor algorithm  46  and from ERA-Interim suggest similar daily ET values (i. e. , ~3. 5 and ~3. 2 mm day -1 , respectively) and similar deviations from the mean of the reference datasets over the Amazon  67. Here, the "reference datasets" refers to a combination of observational-based ET estimates, ET products from land surface model output, and ET from atmospheric reanalyses  67. We also estimate the ET based on the fifth generation ECMWF re-analysis (ERA5) by using the latent heat flux (J m -2 day -1 ) from ERA5, which is then converted to ET (mm month -1 ; herein ERAET). Thus, we have nine distinct estimates of ET-P based on different combinations (Table  S2 ), and calculate the RMS error in the fit between these nine ET-P and ET-P estimates based on AIRS δD_004.
Acknowledgements This research was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration. Part of this research was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract ( 80NM0018D0004 ) with the  National Aeronautics and Space Administration. We thank  Adam J. Purdy  for providing the PT-JPL data. We also thank  Fernando Galvan  and  Lingcheng Li  for map visualization. AIRS HDO/H 2 O data were processed as part of the  NASA Tropospheric Ozone  and its Precursors from Earth System Sounding (TROPESS). MS was partly supported by the  U. S. Department of Energy Office of Science Biological and Environmental Research  as part of the  Terrestrial Ecosystem Science Program  through the  Next-Generation Ecosystem Experiments (NGEE) Tropics  project.
The overall format of the paper should be improved, since various symbols and equations are extremely hard to read or understand, particularly Bayesian-related equations. The authors have done a great and in-depth survey on supervised, unsupervised and semi-supervised concept drift detection models. This can potentially be improved to become a separate survey paper. The authors should also consider making the code used in the experiments publicly available and provide a detailed guidance on how the results can be replicated. The repository https://github. com/Usman07442/AE-DDM-Experiments only contains information on the datasets used, not the actual implementation of the proposed framework. Within their survey on concept drift detection models, there are discrepancies in the citation format.
Specific comments The figure 1 that the authors opted to use to illustrate "real drift" and "virtual drift" does not fully represent the difference between the two. The previous paragraph should also be divided into bullet points with separated definition for each kind of drift to enhance readability. At the end of page 4, when the authors say that "Supervised drift methods like DDM, RDDM, LFR and others […], these labels are not available immediately after the prediction most of the time and it is not possible to detect drift in real time using techniques", this is not necessarily true. For example, with River, an online machine learning package written in Python, the algorithms are designed to provide labels immediately once the predict_one command is called, which allows such models to be effective in real time.
"Motived by …" should be changed to "Motivated by …". "The decoder part is the exact replica of the encoder part" makes sense in terms of idea, but is incorrect in terms of phraseology. The encoder part is the "mirror" of the decoder part, in which it decodes the smaller space into data that is exactly or as near to the original data as possible. The function g(h) cannot be read properly. The reviewer disagrees with what the authors mentioned in page 6 that "Another limitation of this work is that the same threshold is used for different datasets and we have experimentally observed that each dataset as well as each class data has its own reconstruction loss pattern and threshold. " Regarding online machine learning problems, we do not always know previously what is the distribution and characteristics of data streams look like; as such, having algorithms that work with various datasets under the same threshold would be the optimal path to follow.
The authors should also illustrate at which scale the reconstruction loss is compared to the original magnitude of the data points. A reconstruction error of 0. 11 on data points with small and large magnitude are totally different scenarios. How are the batch threshold and instance threshold values calculated for positive AE and negative AE in Stagger dataset? This should be showcased explicitly within every experiment since the thresholds would have significant impact on the performance of the algorithm. Minor remark: In figure  9 , instead of writing "call algorithm XX", the authors should instead state the purpose of the algorithm to make the flowchart more comprehensive. In algorithm 6, why does the author fix he number of consecutive batches that exceed thresholds to 3? What would happen if this number is increased? In the GAUSSIAN dataset, the mean and standard deviation of each feature should also be explicitly mentioned.
Statistical analysis The statistical analysis was performed with JamoviÓ (1. 6. 23. 0 version) and R StudioÓ (1. 2. 5033 version) software. Food choice motivations and psychological variables (i. e. , symptoms of disordered eating, coping strategies, and quality of life) were compared by diet (i. e. , omnivore, partial vegetarian, strict vegetarian, and vegan). Chisquare tests were used for categorial data with an examination of the adjusted standardized residuals (an absolute value greater than 2 indicated a significant over-or under-representation) and of the effect sizes with the V of Cramer (interpretation according to the degrees of freedom;  [27] ). For continuous variables, an analysis of variance (one-way ANOVA) with Tukey's pairwise comparisons tests were used. h 2 measurement was also used to calculate the effect sizes.
Hydrographic data To relate sea level variability to subsurface processes, we use the EN4 monthly gridded profiles of temperature and salinity (version EN. 4. 2. 2 with mechanical/expendable bathythermograph (MBT/XBT) profile data bias adjustments from  Gouretski and Reseghetti, 2010)  for the period from January 1993 to December 2020  (Good et al. , 2013). The profiles are based on the objective analysis of hydrographic observations (e. g. , XBT, MBT, bottle, CTD, and Argo). The number of Argo profiling floats in the ocean was growing from a very sparse array of 1000 profiling floats in 2004 to a global array of more than 3000 instruments from late 2007 to the present. This means that maps in 2004-2007 and especially before 2004 are less accurate than maps in 2008-2020. The EN4 temperature and salinity profiles are used to calculate the steric SSH anomalies (SSHST; due to changes in density) by integrating in-situ density anomalies with respect to the time mean and over the upper 1,000 m, which is the depth interval occupied by the NAC.
EOF analysis Empirical orthogonal function (EOF) analysis is widely used to reduce the dimensionality of datasets and to extract the leading (most influential) modes of variability. Individual EOF modes, if found meaningful, are often assumed to describe different physical processes. Here, we use the conventional EOF analysis  (Navarra & Simoncini, 2010)  to identify the leading stationary modes of the low-frequency SSH variability in the North Atlantic. The EOF analysis is applied to both the satellite altimetry (SSH) and the EN4-derived (SSHST, SSHT, and SSHS) data. Because of the scarcity of in situ observations prior to the advent of Argo, only the EN4 data starting from 2004 was used for the EOF analysis. For each mode j, the spatial pattern (map) is represented as a regression map (EOFj) obtained by projecting SSH data onto the standardized (divided by standard deviation) principal component (PCj) time series.
Acknowledgements The delayed-time satellite altimetry maps are processed and distributed by the  Copernicus Marine and Environment Monitoring Service  (http://marine. copernicus. eu;https://doi. org/10. 48670/moi-00148 ). EN. 4. 2. 2 data were obtained from https://www. metoffice. gov. uk/hadobs/en4/ and are  © British Crown Copyright ,  Met Office , provided under a Non-Commercial Government License http://www. nationalarchives. gov. uk/doc/non-commercial-government-licence/version/2/. Argo data were collected and made freely available by the  International Argo Program  and the national programs that contribute to it. (http://www. argo. ucsd. edu,http://argo. jcommops. org). The Argo Program is part of the Global Ocean Observing System. Hersbach, H. et al. , (2019) was downloaded from the  Copernicus Climate Change Service  ( C3S ) Climate Data Store (doi: 10.
Finally, the antenna is 30 • bended in order to focus the energy in the investigated area direction. The R60 Copper Mountain reflectometer is controlled (via serial USB connection) using an UDOO single board computer. The board is powered using a stabilized current source (12V) and a timer (CRONTAB) to allow power/sleep modes for long time monitoring campaign. Indeed, for the presented results, a stack of 10 traces was performed regularly in time (every 5 minutes) where the real and imaginary part of S 11 are recorder over 801 points covering the frequency band 0. 2 GHz -5 GHz. The frequency domain measurement data are inverse Fourier transformed to generate time domain radargrams with a time window of 30 ns. The expected dynamic range of measured data can reach up to 90 dB using the R60 Copper Mountain reflectometer and adopting the step frequency technique.
Electromagnetic modeling For simulating our M-GPR data we used the open source software GprMax2D  (Warren et al. , 2016) , in two dimensions, as presented in  Léger et al. (2020). The EM model was set to 1. 2 m wide and 0. 6 m high, larger than the real model to avoid side effects, filled up to 0. 48 m with saturated sand following the permittivity distribution presented in section 2. 3 Results.
Inflow rates were measured by an electromagnetic flow meter and subsequently recorded by a central control computer. The outflow from each wetland system was measured by counting the number of times a calibrated vessel (Rotring SCS, Bremen, Germany) filled and emptied each hour. Outflow vessel volume was calibrated regularly, at least once per year, to an accuracy of 0. 1 L. All data was recorded by a PLC system. Inflow was recorded with a magnet inductive flow meter (Endress+Hauser, Promag 10) and outflow was measured with a tipping counter. The research facility was equipped with an onsite weather station that measures air temperature, relative humidity, rainfall, air pressure, wind speed and direction, and solar radiation. Measurements were collected every 10 min (600 s). Weather data and flow measurements are from August 1, 2010-July 31, 2020.
The units on the factor 1. 08 are in s/m and the units on the conversion factor 37. 6 are in W/m 2. Eqs. (  2 ) and (3) were used to calculate ET o from the weather data collected at the research site in Langenreichenbach. Data collected every 10 min produced average ET o values over a 10-minute interval. These data were subsequently used to calculate average daily mean ET o. ET o ¼ g T ð Þ Â t L * Â 0:6 Â R G þ 37:6 Â 1 þ 1:08 Â v 2 ð Þ ð Þ Â 1 À U 100 (2) g T ð Þ≈2:3 Â T þ 22 T þ 123 (3) where: t = time interval between measurements, in seconds R G = global radiation, W/m 2 (average over time interval t) L* = specific heat of evaporation, W•s/kg T = air temperature in °C (average over time interval t) v 2 = wind speed at 2 m height, m/s (average over time interval t) U = relative humidity in percent (average over time interval t).
Environmental conditions Relative humidity, air temperature, wind speed, air pressure, and solar radiation data were collected every 10 min from the onsite weather station. These data were used to calculate daily mean values as well as the mean daily reference Penman evapotranspiration ET o (Fig. 4 ). Air temperatures at the site displayed a seasonal, sinusoidal pattern, with daily mean temperatures in winter reaching as low as -15 °C and peak summertime temperatures of +30 °C. Solar radiation is the dominating factor in the calculation of the reference Penman evapotranspiration ET o. The weather station at the research site in Langenreichenbach was situated above the main control building (gray building, center of Fig. 1 ), was  elevated compared to the surrounding land. Reference evapotranspiration rates calculated from the weather data at the research site in Langenreichenbach were more variable and approximately 20 % higher than those calculated from the nearest weather station in Klitzchen, Germany, which is located approximately 4 km from the research site (see Fig.
Acknowledgements The authors would like to thank  Katy Bernhard  of the  Helmholtz Centre for Environmental Research -UFZ  for the long-term operation of the research platform in Langenreichenbach,  Germany. The authors also kindly acknowledge  Tom Headley  for support in establishing a method for plant biomass measurements. Parts of this work have been funded by the  German Federal Ministry of Education and Research (BMBF)  within the project " Leipziger BlauGrün " through Grant  033W110A. Parts of this work have been funded by the  European Commission   H2020  Project  MULTISOURCE (Grant Agreement 101003527 ).