dataesr/scientific-paragraphs-categorization huggingface dataset:
The size of individual plots ranges from 0. 02 ha to 25 ha, with a median size of 0. 20 ha, and they cover a total area of 18,192 ha. The plot measurements took place between 1996 and 2018, and their AGB values were estimated using allometric models deemed appropriate for their forest area by the data providers. The mean plot AGB is 100. 70 Mg ha -1 with a standard deviation (SD) of 158. 31 Mg ha -1. More comprehensive plot summary statistics are shown in Table  S2.
, 2013) ; (b) within ecological regions or biomes  (Whittaker, 1975)  as a function of rainfall and temperature; (c) within strata with and without possible man-made forest disturbances as indicated by a scatterplot of log2-scaled human population density  (Balk and Yetman, 2004 ) against tree cover percentage. IPCC 2006 report. Specific growth data are used depending on the forest type, ecological region, forest age and continent. The second step deals with the different areas of the forest plots and the AGB map support unit (i. e. , the original pixel size or a coarser grid cell). Note that the map provides an estimate of AGB within each support unit, but this may include non-woody and non-forest areas, especially in heterogeneous and fragmented landscapes  (Chave et al. , 2004; Nascimento and Laurance, 2002).
Plot measurement and allometric model errors Non-destructive forest inventory is the traditional method used to estimate tree AGB, but has an uncertainty of 5-44% at the stand scale  (Burt et al. , 2020). Tree measurement errors originate from uncalibrated surveying tools and human errors, which propagate into the allometric model used to estimate AGB from tree diameter (and height) per tree, and then into aggregation at plot-level  (Réjou-Méchain et al. , 2017). The cumulative error from tree measurement and allometric models is termed "measurement error" in this study. To estimate the uncertainty in measurement errors (SD me ), plots with tree data were used to estimate how errors in individual tree measurements propagate into biomass estimates at plot-level. We used data from 8457 plots, ranging from 0 to 25 ha in size and with a total of 267,907 trees.
These plots are within all the major climatic zones (tropics, subtropics, temperate and boreal) and across eight countries, of which the majority are in the tropics. However, plots with tree data constitute only 7. 4% of all plot data used in our analysis. A two-step approach was therefore implemented to predict SD me for all plots using a model calibrated on the plots with tree data. The first step estimated tree-level errors due to uncertainty in tree parameters (wood density, diameter and tree height). Tree wood density data and their uncertainty were obtained from global and regional databases  (Chave et al. , 2009). For trees without height data, stem diameter was used to estimate height, H, using the Weibull height-diameter model (Eq. (  1 )). This three-parameter estimator of H has been tested within tropical forests  (Feldpausch et al.
, 2012) , temperate coniferous forests in Norway  (Mahanta and Borah, 2014)  and China  (Zhang et al. , 2014) , boreal forests  (Zhang et al. , 2018) , and coniferous forests in the Philippine Highlands  (Anacioco et al. , 2018) : H = a(1 -exp( -(DBH/b) c ) ) (1) where DBH is diameter at breast height and a, b and c are fitted coefficients. Errors arising from the parameters in the biomass allometric model, such as model coefficients and residual standard errors, were also considered. These data are derived from the dataset of destructive tree measurements provided in  Chave et al. (2014). The overall error propagation included the probability distributions of the tree and allometric model parameters and was implemented by running 1000 Monte Carlo simulations using the AGBmonteCarlo function of the BIOMASS R package  (Réjou-Méchain et al.
Aggregation to 0. 1 ∘ grid cells The plot data were compared with the AGB maps in 0. 1 ∘ grid cells (referred to below as default AGB maps), which is a spatial scale comparable to those typically used in global carbon cycle and climate models. Non-forest pixels (taken to have AGB = 0) were included for Baccini and GEOCARBON prior to aggregation to avoid biasing the 0. 1 ∘ AGB averages if only forest pixels are used. For each grid-cell i, its average AGB was estimated from the map data by averaging the AGB estimates at each pixel in the cell; and from the plot data by the MVLU estimate under the assumption that the plot data are unbiased. The MVLU estimate is the weighted mean of each plot estimate of AGB, x, inside the grid cell i, where the weight is inversely proportional to the variance of x.
The random forest algorithm We used RF  (Breiman, 2001)  to model and predict bias. RF is a nonparametric ensemble model of decision trees from bootstrapped samples of the training data and produces averaged predictions (RF regression). We implemented RF using the ranger R package  (Wright and Ziegler, 2017). This provides a standard error (SE) of the RF model, calculated using the infinitesimal jackknife approach  (Wager et al. , 2014)  along with a function case. weights that prioritizes data with higher weights when forming the bootstrap samples, and hence the trees of the RF model.
Bias modelling We modelled bias using RF regression and data at 0. 1 ∘ to form weighted bootstrap samples. The model used open access sources of spatially exhaustive covariates that were considered to have a possible influence on bias  (Chave et al. , 2004; Réjou-Méchain et al. , 2014; Santoro et al. , 2015) , all averaged to 0. 1 ∘. We first tested 11 covariates, including the AGB map itself, its reported uncertainty, slope, aspect, tree cover, elevation, rainfall, temperature, biome, longitude and latitude. Using all and partial combinations of the covariates, we created multiple RF models using the default RF hyperparameters. The models were evaluated using a randomly held-out 30% of the 0. 1 ∘ data to assess the proportion of the variance of residuals explained by the model. We then visually inspected the bias for indications of geographic correlation among covariates, as suggested in  Meyer et al.
(2019). After this initial investigation, we limited the covariates to the five listed in Table  1 , which also gives brief metadata on the final covariates. The predictive power of the covariates in the RF model was assessed by the Variable Importance Measure (VIM) and Partial Dependence Plots (PDP). VIM is the mean decrease in accuracy of an RF model after data permutation of a covariate, while a PDP shows the marginal effect of covariates on bias prediction. We normalized and ranked the VIM for every AGB map. The PDPs are displayed as matrices, color-coded with bias and with the axes labelled by the values of a covariate pair, e. g. , bias plotted against AGB map and tree cover (Fig. S3 ). Under the assumption that the error in the estimated bias is normally distributed, we derived the 95% confidence intervals (CI) of the predicted bias ( MD) using the SE from the RF model (Eq.
01 ha plots in the tropics. As well as plot size, the number and spatial spread of plots inside a map pixel affects sampling errors  (Naesset et al. , 2015; Bradford et al. , 2010). Several randomly placed samples may be better at capturing the mean AGB of a forest region than a single large sample covering the same total area  (Nascimento and Laurance, 2002). LiDAR data may also be useful as reference data since LiDAR-based AGB maps typically cover substantial areas and hence provide samples covering the whole range of AGB in a landscape. This will prevent biases arising from preferential sampling, which is often implicit in the selection of research plots  (Duncanson et al. , 2021). Whenever available, these maps would be preferred over the research plots themselves as reference data. Uncertainty from plot temporal adjustment was largest for the AGB maps of 2000 and 2017 because of the longer periods between the map epoch and plot inventory date (Table  S1 ), but was a small contributor to total plot uncertainty.
Similar behaviour for the model-based approaches used by GlobBiomass and CCI Biomass may reflect higher sensitivity of radar backscatter to AGB and reduced soil   S2 ). Each circle represents an AGB bin and its size denotes the number of grid cells in the bin, while the whiskers correspond to the 25 th and 75 th percentile range of the map AGB. effects in this AGB range. GEOCARBON has the closest match to plot data for AGB >200 Mg ha -1 , possibly because its bias removal method used a plot dataset in the tropics which may overlap with our plot data. However, this effect is not easy to quantify as here the comparison is with plot data after the harmonization process (Section 2. 2).
Strengths and limitations of the framework A core GCOS principle is that estimates of AGB should as far as possible be unbiased. This study provides a comprehensive framework for meeting this principle by estimating the bias and uncertainty in AGB maps (Fig. 1 ). It can be adapted to the requirements of different map users or producers  (Herold et al. , 2019)  but requires estimates of uncertainty in both plots and maps, and careful vetting of the quality and suitability of plot data. Open source tools estimating plot uncertainty (e. g. , BIOMASS and PlotToMap) are of great value for this. BIOMASS has not been widely tested other than in tropical regions, though is currently being tested in extra-tropical forests. We also provide an interactive online tool for users of the framework and open source software (which can be readily updated) that offers more flexibility in pre-processing plot data and comparing plot and map AGB estimates (https://github.
Also shown are estimates derived from FRA data for these three years. The forest areas of map-based AGB estimates are set as the closest possible area to the FRA forest area based on a 0. 1 ∘ tree cover threshold. The analysis includes all pantropical countries but without the pantropical portions of China and Australia. long-term AGB change estimation based on differences in AGB maps is likely to be unreliable. Further analysis of AGB change from these estimates will be addressed in a follow-up study. The limitations in the uncertainty framework reported here pertain to the plot dataset, plot selection bias, methodological limitations and data requirement issues. Most plot data with tree-level measurements lack tree height, so we estimated height with the Weibull model, but this model may not apply in all biomes, such as woodlands and mangrove forests.
We also lacked tree-level data in non-tropical regions, so estimates of measurement errors for plots in these regions are subject to revision. The fact that the plot dataset in the boreal regions is concentrated in two countries may also limit global application of the framework. Plot selection bias will arise if the plots inside a 0. 1 ∘ grid cell do not provide a random sample of the AGB within the cell. For example, if they are selected to lie within high AGB areas within a diverse forest landscape, their weighted average would overestimate the AGB at 0. 1 ∘. Research plots, which make up 4% of the total dataset but 24% in the tropics, are particularly prone to this effect. To analyze how this might affect our analysis we examined the variability of tree cover at grid scale and plot locations, and treated this as a proxy for AGB variation  (Avitabile and Camia, 2018).
This analysis yielded a set of grid cells without preferential samples (referred to as strict filtering of the plot dataset; see Fig. S1  for the specific steps). Assessment of the GlobBiomass map at pantropical and global scales against the filtered plot dataset (based on the tree cover for the same epoch  (Hansen et al. , 2013) ) gave results differing only slightly from use of the current dataset (see Fig. S1 ). This suggests that preferential sampling had little effect on our analysis. Possible reasons for this are that almost the same number of grid cells were excluded under the current approach and the strict filter (56% and 57% in the pantropics, respectively), and that many of the grid cells selected were the same under both approaches, particularly in tropical high AGB areas e. g.
, 2021). Additional plot data, preferably large plots, are desirable both to compensate for those with high variance and, more importantly, to increase spatial coverage since a large training dataset is needed to capture local AGB error patterns  (Xu et al. , 2016). Selected values from local AGB maps, if they exist, can be added to the training data in under-sampled areas  (McRoberts et al. , 2019b). We also plan to assess different cross-validations of the bias model. In addition, the use of harmonized plots at coarser scales, e. g. , 1 km from forestry concessions  (Ploton et al. , 2020a)  and 25 km from NFIs  (Menlove and Healey, 2020)  are also options, given their extensive forest coverage.
Conclusions 1. The comprehensive uncertainty framework developed in this paper can correct existing AGB maps for bias, within the limitations of the bias model. Such maps, with their associated SDs at coarser scales, are particularly useful in the context of climate and carbon cycle modelling. The analysis of spatial bias and models of spatial error correlation provide valuable information to both map users and producers on local to regional map errors. 2. The estimates of bias in the AGB maps exhibit spatial patterns that largely reflect AGB itself. The bias models would benefit from additional plot data and local AGB maps within poorly represented regions i. e. , LiDAR-based maps will be preferred over plot data whenever available. 3. The spatial uncertainty modelling was hindered by plot AGB uncertainty arising principally from measurement and sampling errors, which tends to be especially large in regions where only small plots are available.
It would be helpful if NFIs included some larger plots (e. g. , >1 ha) to serve multiple purposes, including the assessment of global AGB maps. 4. Both map-based and FRA estimates of pantropical AGB show a decline from 2000 to 2017, and become increasingly close with time, despite the datasets and methods used being quite different for different map epochs. However, there is still a difference of at least 6. 9 Pg between the map-based and FRA estimate in 2017.
Acknowledgements This study has been partly supported by the  IFBN and CCI Biomass  projects funded by  ESA. We thank  Michael Keller  and  Alessandro Baccini  for their data provision, and  Maxime Réjou-Méchain  and  Philippe Ciais  for their technical suggestions. Plot data from Russia was processed within the framework of the state assignment of the  Center for Forest Ecology and Productivity of the Russian Academy of Sciences  (no. -18-118052590019-7), and was financially supported by the  Russian Science Foundation  (no. 19-77-30015 ). Shaun Quegan  and  Joao Carreiras  were supported by the  UK Natural Environment Research Council (NERC) , through agreement #  PR140015  between  NERC  and the  National Centre for Earth Observation. We thank the  VERIFY  project supported by the  European Commission ,  Horizon 2020 Framework Programme  (no.
ABSTRACT: Neutral loss (NL) spectral data presents a mirror of MS 2 data and is a valuable yet largely untapped resource for molecular discovery and similarity analysis. Tandem mass spectrometry (MS 2 ) data is effective for the identification of known molecules and the putative identification of novel, previously uncharacterized molecules (unknowns). Yet, MS 2 data alone is limited in characterizing structurally related molecules. To facilitate unknown identification and complement the METLIN-MS 2 fragment ion database for characterizing structurally related molecules, we have created a MS  2  to NL converter as a part of the METLIN platform. The converter has been used to transform METLIN's MS 2 data into a neutral loss database (METLIN-NL) on over 860 000 individual molecular standards.
7 This is of significant practical relevance. A user who would try to identify an unknown based on a MS 2 database similarity search would not succeed in obtaining structurally relevant matches. However, retrieving this structurally useful information is possible by analyzing the differences between the molecular ion and the fragment ion, or better known as the neutral loss (NL) and symbolized by Δm/z. NLs 1,2 constitute a rich resource and have already been widely used in proteomics, pharmacology, and metabolomics for over three decades  1, 2, [8] [9] [10] [11] [12]  as represented by over a thousand papers on the topic. Yet, even though mass spectrometry-based NL analysis has been extensively applied, no small molecule MS 2 to NL conversion programs exist, nor any comprehensive library of NL spectra.
Unlike MS 2 data, which projects the m/z values and intensity of the precursor and each fragment, for NL data Δm/ z, is projected as the difference between precursor ions and its respective fragment ions. It also can be generated as a difference between fragment ions. 10 he new METLIN NL converter has been created as a general resource and to convert METLIN's 860 000 MS 2 small molecule molecular standards database into a mirrored NL database (METLIN-NL) to facilitate neutral loss searching. The NL data was derived across a broad range of standards representing hundreds of different chemical classes. 3, 13 The converter was designed to input METLIN's MS 2 data and convert it to METLIN-NL spectra (e. g. , Figure  1  asymmetric dimethylarginine (ADMA)) by using the converter to calculate the differences between the precursor molecular ion and the fragment ions in the experimental MS 2 mass spectra (Figure  1A ).
The NL spectra (NL intensity versus Δm/z) were created (e. g. , ADMA Figure  1B ) with the NL intensity (NL intensity ) using the fragment ion intensities from each precursor/ fragment generated NL (Δm/z). It should be noted that not all precursor to fragment peaks represent a true NL between the precursor and fragment ions, and therefore some of the peaks in the NL spectra can also be considered (as recently described  10  ) hypothetical neutral losses. The MS 2 to NL converter (https://metlin. scripps. edu and https://github. com/masspec/MS2ToNLConverter) allows users to view a single MS 2 or NL spectra or do a comparison between two MS 2 or NL data. When using METLIN IDs, the MS 2 and NL data are already calculated, but when using CSV files the MS 2 data is automatically converted to NL.
To facilitate these analyses, METLIN-NL is built on a Linux platform with the initial version of the graphical user interface (GUI) created using Highcharts, HTML, JQuery, MySQL, and PHP. The GUI allows for comparative analyses between different compounds including neutral loss data (NL int versus Δm/z) as well as MS/ MS data (Frag int versus m/z) in both positive and negative ionization modes. The GU also offers visualization either at each individual collision energy, or a "composite spectra", that constitutes all spectra across the multiple collision energies. Once a spectrum (or spectra) has been generated, users will be able to hover the cursor on each peak to obtain detailed information about m/z, intensity, ionization mode, the compound's name, and collisional energy values. The user input, for example, CSV file, for the Web site https://metlin-nl.
scripps. edu/ requires a compound name, masses with intensities, collision energy, positive/negative mode, and precursor value. Users have access to two downloadable CSV file to demonstrate formatting. The converter operates in the following modes and allows users to create/compare the following data types (Table  1 ). METLIN-NL is a compilation of NL intensity versus Δm/z spectra generated from METLIN's eight distinct MS 2 data sets created from 860 000 standards. 3 This compilation is represented within METLIN-NL at four different collision energies and in both positive and negative ionization modes. The rationale behind providing multiple conditions is that MS 2 collision energies have not been standardized and such broad acquisition parameters are required to represent the output across different instrument types.
An additional rationale for the array of conditions is that different molecules can fragment differently depending on the collision energies thus METLIN provides a broad range of empirical data across its 860 000 standards. It is worth noting that all of METLIN's MS 2 data is empirical data and has not been generated from predictive in silico-based approaches. A secondary set of METLIN-NL data has also been accumulated based on precursor minus fragment ion transitions as well as all possible fragment-to-fragment ion transitions to provide a more comprehensive set of experimentally derived structural data. Unlike the original METLIN-MS 2 database, METLIN-NL represents a translation that more effectively enables the molecular annotation of unknown molecular entities since NL data inherently corrects for molecular weight differences.
To test the utility of METLIN-NL we examined two different types of molecular structures, oxylipins and a pharmaceutical (statin) drug and its demethylated metabolite. Oxylipins 14 represent a class of highly active lipid metabolites ubiquitous in humans and plants, and specifically the phytoprostanes (PhytoPs) class of oxylipins resemble prostaglandin-like compounds that are found in seeds and vegetable oils derived from oxidative cyclization of α-linolenic acid. Since PhytoPs are a class of highly structurally related oxylipins and are suspected to have additional unidentified analogs,  [14] [15] [16]  we chose them to demonstrate the utility of METLIN-NL. Tandem MS and NL data were recently generated on a set of PhytoPs, including the structural analogs    2 ). When trying to extrapolate/correlate the observed tandem MS spectra of the two PhytoPs, classic similarity searching was of very limited value providing only one overlapping ion, even though some fragments presented an expected two Dalton difference (Figure  2A ).
This exemplifies that two structurally very similar molecules can yield highly different MS 2 spectra limiting similarity searching possibilities and thereby severely impacting the usefulness of this approach for the identification of chemically closely related substances. However, NL similarity analysis yielded multiple overlapping NLs (Figure  2B ). Further analysis of the tandem MS data as well as the molecular weight difference between the two molecules being 2 Da were consistent with 16-keto 16-B1-PhytoP. This NL data (unlike the MS 2 data) helped to easily correlate the two molecules, and the distinguishing NL and fragment ions   exclusive to 16-keto 16-B 1 -PhytoP and 16-B 1 -Phyto provided significant structural information. Another example with dimethyl sphingosine and sphingosine C20 (Figure  3A, B ) further shows the synergy that MS 2 and NL data can have between structural analogues.
Dimethyl sphingosine and sphingosine C20 (A) MS 2 and (B) NL data each have the same elemental composition yet distinct structures. In this case, both the MS 2 data and the NL data show multiple overlapping peaks thus representing an where both types of complementary data provide confirmation of the structural similarity yet each has unique distinguishing information. The purpose of having a large database is to help reduce the need for speculation and allow for the rapid identification of molecules. However, since many molecular structures are not represented in any database, similarity analyses offer an alternative in the preliminary characterization process. This process extends beyond naturally occurring molecules and can be applied just as readily to xenobiotics and other chemical entities.