dataesr/scientific-paragraphs-categorization huggingface dataset:
The Pinus sylvestris trees have been scanned in needle-on condition, the other species were scanned without foliage. • Clouds: https://zenodo. org/record/5131717/files/demolClouds. zip S4 Dataset Disney et al. 2018 data  [38, 39]  An Acer pseudoplatanus  [39]  tree cloud of a large and complex individual with DBH larger 1 m is used to show statistical analysis potential. The tree is located in Wytham Woods, UK. • Clouds: https://zenodo. org/record/5131717/files/wythamCloud. zip S5 Dataset High resoluted leaf-less plot data. A full plot in leaf off conditions. The plot does not have ground truth data in attached, but the quality of tree segmentation as well as DTM and QSM modeling can be visually assessed. • Clouds: https://zenodo. org/record/5131717/files/leibzigCloud. zip 1. 1. 1 Processing scripts S1 Processing scripts SimpleForest scripts to process S1 Dataset.
• Erythrophleum fordii denoising scripts: https://zenodo. org/record/5131717/files/hackenbergErythrophleumDenoisingScripts. zip • Pinus massoniana denoising scripts: https://zenodo. org/record/5131717/files/hackenbergPinusDenoisingScripts. zip • QSM modeling script: https://zenodo. org/record/5131717/files/hackenbergQsm. xsct2 S2 Processing scripts SimpleForest scripts to process S2 Dataset. • Denoising scripts: https://zenodo. org/record/5131717/files/deTanagoDenoisingScriptsDenoisedClouds. zip • Poisson reconstruction buttress script: https://zenodo. org/record/5131717/files/deTanagoButtressPoisson. xsct2 • QSM modeling script: https://zenodo. org/record/5131717/files/deTanagoQsm. xsct2 S3 Processing scripts SimpleForest scripts to process S3 Dataset. • QSM modeling script: https://zenodo.
org/record/5131717/files/demolQsm. xsct2 S4 Processing scripts SimpleForest scripts to process S4 Dataset. • QSM modeling script: https://zenodo. org/record/5131717/files/wythamQsm. xsct2 S5 Processing scripts SimpleForest scripts to process S5 Dataset. • QSM modeling script: https://zenodo. org/record/5131717/files/leibzigQsm. xsct2 1. 1. 2 Validation scripts S1 Validation scripts SimpleForest scripts to validate results of S1 Processing scripts. • Volume validation script: https://zenodo. org/record/5131717/files/hackenbergValidation. R S2 Validation scripts SimpleForest scripts to validate results of S2 Processing scripts. • Volume validation script: https://zenodo. org/record/5131717/files/deTanagoValidation. R S3 Validation scripts SimpleForest scripts to validate results of S3 Processing scripts.
• Volume validation script: https://zenodo. org/record/5131717/files/demolValidation. R S4 Validation scripts SimpleForest scripts to validate results of S4 Processing scripts. • Statistical plotting script: https://zenodo. org/record/5131717/files/wythamAnalysis. R S5 Validation scripts SimpleForest scripts to validate results of S1 Processing scripts, S2 Processing scripts, S3 Processing scripts. • Combined results data table: https://zenodo. org/record/5131717/files/tableAll. csv • Volume validation: https://zenodo. org/record/5131717/files/ValidationScriptAll. R 1. 2 Software S1 Software Software code repository. • Under the GPL version 3 license: https://gitlab. com/SimpleForest/computree/-/blob/master/pluginSimpleForest/GPL_v3_template • we provide source code with compilation instructions for the here presented SimpleF orestv5.
Abstract in French Estimation de l'ensemble des coûts économiques des espèces exotiques envahissantes en Australie. L'histoire des introductions intentionnelles et accidentelles des espèces exotiques envahissantes en Australie a un coût économique élevé. La quantification de l'ampleur de ce coût associé aux pertes directes ainsi qu'aux dommages demeure pourtant inconnue. La difficulté d'arriver à une estimation robuste du montant total est exacerbée par un échantillonnage insuffisant et un manque de protocoles pour déterminer la robustesse des estimations de coûts individuels. Nous fournissons le premier bilan des coûts associés aux espèces envahissantes à l'économie australienne depuis les années 1960, à partir de la base de données InvaCost récemment publiée, enrichie d'estimations supplémentaires.
Abstract in Portuguese Avaliacão detalhada dos registos de custos económicos associados a espécies invasoras na Austrália. O legado de introduções deliberadas e acidentais de espécies exóticas invasoras na Austrália tem resultado em custos económicos consideráveis. Contudo, calcular a magnitude dos custos associados a perdas diretas e danos, bem como dos custos associados com intervenções de gestão, não é imediato. Este desfazamento está relacionado com o nível indeterminado de confiança nas estimativas de custo e com a sub-amostragem. Nós providenciamos a primeira análise detalhada dos registos australianos de custos associados a espécies invasoras desde os anos 60, com base na publicação recente da database InvaCost e respectiva informação complementar, para o total de 2078 registos únicos de custo.
Such information can help to assist in setting priorities by policy-makers and organisations for managing invasive species in some of the most impacted countries. Recently, the InvaCost database was developed to provide the most comprehensive and standardised compilation of invasion costs globally  (Diagne et al. 2020b ). This advance now addresses the aforementioned limitations by presenting economic costs at a global scale, yet with sufficient resolution to enable assessment in more granular national, taxonomic and socioeconomic contexts. Further, InvaCost allows for assessment of the reliability of cost estimates, as well as for whether costs are predicted to occur or have been empirically observed. While broad-scale perspectives of the economic costs of invasive species are needed because of the transboundary nature of invasions, national or regional assessments are still required in much greater detail  (Diagne et al.
g. ,  Sinden et al. 2004 ). Moreover, most assessments have been reliant on flawed assumptions  (Sagoff 2008; Holmes et al. 2009 ) and extrapolations  (Pimentel et al. 2001)  or have applied more top-down approaches to estimate costs by sector, rather than to divide the estimates among species, regions, sectors or decades  (McLeod 2004; Sinden et al. 2004; Gong et al. 2009; Hoffmann and Broadhurst 2016; Llewellyn et al. 2016 ). Here we focus on Australia and its territories to provide the first detailed assessment of the reported economic costs of invasive species since the 1960s, based on records extracted from the recently published InvaCost database  (Diagne et al. 2020b) , combined with both an independent database of costs restricted to invasive herbivore species (previously unpublished) and recent data describing the costs of invasive plants and other disease-causing agents.
Data collection To determine the cost of invasive species to the Australian economy, we used cost data collected in the InvaCost database  (Diagne et al. 2020a, b)  (n = 2,419 entries) concerning the global costs of invasive species, based on published literature, enabling comprehensive quantification of costs associated with invasive species at various spatio-temporal scales. Of these, 877 (36%) entries pertained to Australia. The data in InvaCost were collected following a series of literature searches using the Web of Science platform (webofknowledge. com), Google Scholar (scholar. google. com) and the Google search engine (google. com) and all the retrieved costs were converted to a common, up-to-date currency (2017 US$; data. worldbank. org). We complemented the InvaCost data in three ways.
We first added supplementary cost data from new references containing cost information (~ 2300 entries; https://doi. org/10. 6084/m9. figshare. 12928145). Next, we added data from the "Costs of Invasive Herbivores in Australia" database compiled by Biosecurity South Australia in 2018. The latter is an unpublished database compiled by L. A. to collate peer-reviewed and government documents reporting estimated costs specifically for 'invasive' herbivores [this can include native species, which compete with human interests in some cases -for example, kangaroos (various species, notably Osphranter and Macropus spp. )]. That database also includes pigs (Sus scrofa) and birds, even though these species are not all strictly herbivores. Based on the top five commodities in each of the categories of livestock, crops and horticulture as a starting point, the impacts from pest animals on those commodities were compiled for each.
Estimates were identified using Google Scholar and Google search engine for peer-reviewed papers, conference papers, surveys and reports (e. g. Australian Bureau of Agricultural and Resource Economics, Invasive Animals Cooperative Research Centre, government and industry reports). As a last step, we augmented the database with additional, missing cost estimates identified during the review process, as well as additional searching. We included all new costs following the structure, decision points and rules of the original InvaCost data  (Diagne et al. 2020b ). Many of the additional costs were derived from a single, large report on weeds of cropping systems by  Llewellyn et al. (2016). The reporting units used in that report were the Grain Research and Development Corporation agroecological zones and some of these zones crossed state-government boundaries.
gov. au/statistics/historical-data. html. We calculated the relevant consumer price index by taking the 12-month average change to November 2020 reported at bls. gov and applied it to the 2019 consumer price index reported by the chosen InvaCost data source (data. worldbank. org). We reviewed all sources, as well as the references they cited, to identify additional sources. Each entry recorded: (i) species identity ('general' if unspecified); (ii) reported cost (including range if available; no hypothetical costs included); (iii) jurisdiction (including area of coverage if provided); (iv) applicable year(s) (set to year of publication if not provided); (v) implementation (observed or extrapolated); (vi) method (field, desktop, both); (vii) verification (whether approach could be identified/repeated); and (viii) type  (control, loss, research, damage, mixed).
After combining the separate databases and standardising/aligning columns, we removed obvious duplicate cost estimates (i. e. same cost figures from (non-)identical sources) following previous protocols  (Bradshaw et al. 2016; Diagne et al. 2020b). Following our data processing (see below), we finished with a total of n = 2257 unique entries pertaining to Australia for analysis (database available for download at https:// doi. org/10. 5281/zenodo. 4455979).
Estimating total costs Deriving the total cumulative costs of the impacts and management of invasive species over time requires considering the temporal period to which a particular cost estimate applies. We calculated the duration of a cost as the number of years between the probable start and end years provided in the full database. When the exact start year was unknown, we conservatively considered the year of publication of a primary data source as either the start year or the end year, to which the duration (if mentioned) in number of years was considered (by adding or subtracting the number of years) to derive either, respectively, the end or the start year  (Diagne et al. 2020b ). We did not use data describing costs prior to 1960 to avoid inconsistencies in currency conversion. We also removed all costs identified as 'avoided' because of a given intervention (i.
e. unrealised costs). To calculate the total cumulative cost, we first recalculated all the annual costs for the defined periods of their occurrence using the invacost package in R via the expandYearlyCosts function  (Leroy et al. 2020 ) and then summed them to obtain total costs. We also estimated the invasion costs for a series of sub-categories by summing all entries according to six descriptive columns in the database: (1) method reliabilitythe perceived reliability of cost estimates, based on the type of publication and method of estimation (low or high); following  Diagne et al. (2020b) , 'high reliability' is accorded if either provided by pre-assessed materials (e. g. peer review, official reports) or using a documented, repeatable and/or traceable method when provided in other grey literature; (2) region -here, we split costs by major political unit in Australia (States and Territories), as well as costs not associated with any particular unit (i.
A more detailed description of the methods we applied is provided in  Diagne et al. (2021). We applied these five different models to both the entire cost dataset for Australia, as well as the highly reliable, observed costs only, to predict model-averaged 'final' (for 2017)  (Diagne et al. 2021 ) estimated costs, based on the temporal trends of the full and subset data. This incorporates both parameter uncertainty estimated in individual models, as well as model uncertainty regarding the true underlying fit. We did this in two ways: (1) we first calculated Akaike's information criterion weights  (Burnham and Anderson 2002)  for the three likelihood-based models (ordinary least-squares regressions and generalised additive model) and (2) using the root mean-squared errors as weights to calculate a weighted-mean cost in 2017 (all five models).
Total cost Since 1960, the total estimated cost of invasive species to Australia was US$298. 58 billion (2017 value), based on 2078 unique entries (after removing 179 records pertain-ing to avoided costs) in the combined database (6674 expanded yearly values), which is approximately equivalent to AU$389. 59 billion (2017 average exchange rate). Of the total costs, the majority (91. 6%) were observed (US$273. 37 billion) rather than predicted or extrapolated ('potential'; US$25. 21 billion) (Fig. 1a ). Of the observed costs, most (61. 3%) were considered highly reliable (US$183. 04 billion). Considering all costs regardless of reliability and implementation type, 27. 6% of the total (US$82. 29 billion) was not attributable to a single kingdom or was unspecified (Fig. 1b ). This arises mainly from a multi-species assessment of costs of invasive species across all of Australia  (Hoffmann and Broadhurst 2016).
Aside from the costs not clearly associated with a particular State or Territory (i. e. nation-wide or not specified), Western Australia had the highest total costs (52. 7%) when considering all costs (US$17. 88 billion) (Fig. 2a ) -69. 3% of this value is attributed to rats Rattus rattus (US$12. 39 billion), but > 99% of this estimate is considered to be of low reliability. When considering only the highly reliable, observed costs, New South Wales had the highest costs (US$5. 25 billion), followed by Western Australia (US$4. 58 billion) and Victoria (US$3. 09 billion) (Fig. 2b ). There was an approximate power-law relationship between the number of unique database entries and the total costs per political unit for both all costs combined (Fig. 2c ) or highly reliable, observed costs only (Fig.
2d ). These relationships indicate that, with an increase of one order of magnitude in the number of estimates, the estimated costs increase on average by 2. 0 (all costs) or 1. 9 (highly reliable, observed costs) orders of magnitude. These power-law relationships were also evident for the cumulative data over time (Suppl. material 1: Fig. S1 ). The magnitude-order increase in costs with the number of database entries appears to be driven mainly by the variation in land surface area among political units (Suppl. material 1: Fig. S2 ); however, there is no relationship between costs and the number of database entries per unit area (Suppl. material 1: Fig. S2e, f  ), suggesting that the intensity of assessment of costs among political units is not systematically different. The Australia-wide or unspecified (to State/Territory) values probably represent some inevitable overlap with the cumulative estimates from the different regions; however, it is impossible to discern to what extent given unspecified attribution in many national-scale analyses (e.
material 1: Table  S1 ). Various fungal rusts, smuts, rots, mildews and other plant pathogens were also featured in the database, accounting for > $697 million of the total reported costs (but only $25. 1 million of the highly reliable, observed costs) (Fig. 3a ). These included the Dothideomycetes (e. g. banana freckle disease Phyllosticta cavendishii), Sordariomycetes (e. g. wheat crown rot Fusarium pseudograminearum), Pucciniomycetes (e. g. wheat stripe rust Puccinia striiformis), Agaricomycetes (e. g. rhizoctonia disease Rhizoctonia spp. ), Exobasidiomycetes (e. g. grass smut Tilletia spp. ) and Leotiomycetes (powdery mildew Blumeria graminis). The costliest species also vary among States/Territories (Fig. 4 ; also see Fig. 5 ). Mammals (cats Felis catus, red foxes, rabbits) are the costliest species only for Australian Capital Territory and New South Wales (Fig.
7a ). Although highly variable from decade to decade, this equates to an average decadal increase of ~ 6. 3-fold (or 3. 2-fold, based on the slope coefficient for the linear robust regression only to compare directly to the 3-fold increase estimated from the global dataset)  (Diagne et al. 2021). Taking only the reliable, observed costs, the average annual cost increased from over US$52. 35 million in the 1970s to US$15. 12 billion during the last decade (Fig. 7a ) or an average 6. 0fold increase per decade (or 1. 8-fold, based on the slope coefficient for the linear robust regression). This translates into a mean annual cost of US$5. 85 billion (all costs) or US$3. 58 billion (reliable, observed only) over the study interval (Fig. 7a ). Examining the temporal trends in the observed, reliable costs only for three of the main taxonomic groups (plants, mammals, insects) shows the general increasing trend, although the most recent decade's increase is driven primarily by costs attributed to plants (Suppl.
material 1: Fig. S3 ). For both all-costs and observed, reliable datasets, the general additive model had the best fit assessed using the highest Akaike's information criterion weights (wAIC). However, the quadratic ordinary least-squares model had the best fit for the highly reliable, observed costs, based on the lowest root mean-squared error (RMSE; Table  1 ). Using these weights to predict the annual costs in 2017 for both datasets, those based on wAIC are dominated by the GAM prediction, whereas those based on RMSE weights accord relatively more importance to the quadratic models (Fig. 7b, c ). For the all-costs dataset, the estimated annual costs in 2017 are US$18. 77 billion (US$6. 09 billion-US$57. 91 billion) according to wAIC or US$17. 88 billion (US$7. 56 billion-US$45. 44 billion) according to RMSE (Fig.
There are an estimated 2700 exotic plant species established in Australia, of which > 400 are declared weedy or noxious  (Hoffmann and Broadhurst 2016). Our database contained highly reliable cost estimates for only ~ 100 species of declared weeds and many weeds did not have species-specific costs as described above. The cost of controlling and the damage done by weeds to the Australian agriculture sector alone are estimated at ~ AU$4 billion year -1  (Sinden et al. 2004; Hoffmann and Broadhurst 2016). However, from the perspective of single species, exotic mammals dominate the costs, with cats and rabbits, in particular, showing some of the highest estimates across the entire sample across Australia. For rabbits, it has been estimated that, without the highly successful biological control programme started in the 1950s, the impacts of rabbits in Australia would have been at least US$53.
Indeed, government investments typically target new incursions first, meaning that many of these are unlikely to be captured in the relevant literature. As most cost estimates are damage arising from invasive plants (weeds), it is understandable why management-related costs represent such a small proportion of the total. However, one invasive mammal species is problematic in this regard -cats. According to the definition of 'reliable' provided for the overall InvaCost database -"Peer-reviewed articles and official documents (e. g. institutional or governmental reports) are likely validated by experts before publication. We assumed, therefore, that all cost estimates collected from these materials may likely be of high reliability"  (Diagne et al. 2020b )we were objectively obliged to include the damage estimate of US$5.
95 billion for this species from  Pimentel et al. (2001). However, that particular estimate was based on an unverified national population of 18 million feral cats and a subjective value of a bird eaten of US$30 (amount to US$540 million year -1 )  (Gregory et al. 2014). The subjective extrapolation of the costs of cats was also noted for the USA  (Fantle-Lepczyk et al. 2021). Compared to the global estimates  (Diagne et al. 2021) , the relatively well-sampled region of Oceania represents ~ 8% (range: 3-22%) of the total average annual costs globally in 2017 according to our database. Further, we found that Australia's rate of cost increase was up to ~ 2 times the rate of cost increase estimated from the global dataset  (Diagne et al. 2021) , although this observation might be explained in part by a lack of data in other regions compared to relatively well-studied Australia.
2021 ) -in other words, the more economic assessments are done, the more costs are discovered. While this could arise in part from the increasing rate of scientific and related publishing over the last 50 years  (Richardson and Pyšek 2008) , under-sampled or under-assessed species and regions will necessarily underestimate total costs. This particularly holds true to aquatic and semi-aquatic alien taxa in Australia, with the majority (99. 9%) of costs attributed to a particular habitat (i. e. excluding mixed-habitat costs) being terrestrial (observed, highly reliable costs) and terrestrial taxa dominating in most regions. On the global scale, this aligns with the under-representation of aquatic invaders relative to terrestrial ones  (Cuthbert et al. 2021b ). The second line of evidence is that many well-known invasive species established in Australia have no associated cost estimates in the database.
For example, there was not a single estimate from the Reptilia in the database, yet species like red-eared sliders (Trachemys scripta elegans) and corn snakes (Pantherophis guttatus) are potentially costly species in some States of Australia  (García-Díaz et al. 2017; Toomes et al. 2020). Neither were pet trade-sourced bird species like rose-ringed parakeets (Psittacula krameri)  (Vall-llosera et al. 2017; Toomes et al. 2020)  or fish pests, such as European carp (Cyprinus carpio)  (Koehn 2004) , identified in our cost database. Neither have native birds  (Bomford and Sinclair 2002)  or insects  (Gu et al. 2007 ) that can heavily damage various crops been adequately assessed for costs (apart from the Queensland fruit fly). Indeed,  Gong et al. (2009)  reported that birds were the costliest vertebrate group to Australian agriculture.
Conclusions While the major costs of loss and damage arising from invasive species, where tangible, are probably captured reasonably well by our database (the under-sampling bias notwithstanding), management-expenditure estimates are perhaps less reliable. The component of the total costs of invasive species attributed to management expenditure is particularly problematic for several reasons. Indeed, there is no standard procedure for reporting expenditure or costs at any level of government or for private organisations, nor is there a national database of expenditure available  (Hoffmann and Broadhurst 2016). A similar argument could also be mounted for damage and loss assessments regarding a lack of a standardised reporting protocol. As our assessment highlights, the large and growing costs of invasive species to the Australian economy are substantial, but under-estimated because of insufficient coverage and a lack of standardised reporting by management authorities and other agencies.