dataesr/scientific-paragraphs-categorization huggingface dataset:
Some algorithms thus implement contextual classification which involves an estimation of the relationships between 3D points from a neighborhoodoften different from the one used for feature extractionin the training data. They aim at producing spatially consistent classifications of 3D PCs, avoiding the noisy output that individual point classifiers can produce, and thus tend to reach higher accuracies. Examples of such approaches are applications of Associative and Non-associative Markov Networks, Conditional Random Fields,and Markov Random Fields to 3D data. However, modelling 3D spatial relationships is computationally intensive and thus challenging to apply to large 3D datasets. These approaches also depend on the relationships observable in the training data, which makes exact inference of correlations between labels unattainable.
Dual cloud features Dual cloud features describe the geometrical, spectral, height statistics or multi-echo characteristics differences between the neighborhood of the core point in PC1 and PC2. Spectral ratios have been introduced in the context of multi-spectral LiDAR classification  (Chen et al. , 2017; Morsy et al. , 2017b; Wichmann et al. , 2015) , but geometrical, height statistics and multi-echo characteristics are new contributions. We designed them to leverage the bi-spectral information and improve the descriptions of scenes characterized by a different 3D aspect in PC1 and PC2. In TB LiDAR datasets, the NIR and green PCs are most significantly distinct above water and vegetation (fig. 1 ), but they can also be slightly different over other surfaces. This is due to the different surface optical characteristics and the NIR and green laser emitters that can have different angles of incidence or aperture.
Experimental datasets and classes To illustrate the use of 3DMASC for bispectral lidar data classification, we selected two topo-bathymetric lidar datasets, representing one coastal and one fluvial environment, respectively. These two datasets only differ in the type of environment they model. They were both surveyed with a Teledyne-Optech Titan airborne LiDAR with two wavelengths, 532 nm and 1064 nm  (Lague and Feldmann, 2020). The green laser points with a forward pitch of 7°, necessary to avoid strong surface reflection on water and has a beam divergence of 0. 7 mrad. The NIR laser has no forward pitch and a beam divergence of 0. 3 mrad. Consequently the incidence angle, surface sampling and laser spot size is never the same at a given location of the scene for the two lasers. The sensor produces high-density PCs, typically 36 pts/m² on land when combining both wavelengthsand 18 pts/m² under water in a single pass  (Lague and Feldmann, 2020).
Classes definition and 3D annotation We evaluate the performances of 3DMASC on two levels of detail: a primary classification of 5 land coversstrictly identical for both areasand advanced labeling of 11 and 13 types of objects on the Ain and Fréhel datasets, respectively. We chose the classes, depending on the diversity of land and sea covers we could observe in each area. We annotated portions of data manually using visual interpretation of the PCs and the RGB imagery acquired simultaneously using Cloudcompare (Girardeau-Montaut, 2022) including new specificic developments for quick labelling of 3D point clouds. Four training and validation datasetsone for each level of detail of each scenewere created, all labeled and balanced, for the classification experiments. They all contain 2000 points of each label.
To eliminate potential spatial bias due to the use of multi-scale spherical neighborhoods, we forced each training and validation point of the same label to be at least 20 m away, considering we used spheres with diameters up to 15 m. Figure  4  illustrates the resulting sets of points labeled for training and validation. The annotated datasets are available along with the source codes of the plugin and of the scripts used to perform further analysis at the following link https://github. com/pleroy/lidar_platform.
Two groups of labels have similar feature contribution patterns. Ground, bathymetry, and water on one side and vegetation and artificial elements on the other. The first group is mainly identified by multi-echo features and NIR intensity. The second relies primarily on dual cloud featuresmedian intensity differences and distances between points of the two wavelengthsand NIR multi-echo attributes. In both cases, the TB aspect of the datasets is fully exploited: for the Ain, there are as many green PC features as NIR PC features, and for Fréhel, the optimized set includes 4 and 6 features of the NIR and green PCs respectively. NIR PCderived features are more contributive to topographic objects, while both PCs are equally crucial for ground/seabed/water distinction. The experiments on Fréhel also draw more on NIR intensity-derived parameters than the models to process the Ain, in which only one green spectral parameter is involved with low relative importance (Figure  11 ).
The low number of predictors makes the models applicable to large datasets, easily explainable with Shapley values and thus accessible to non specialist users. These characteristics allow 3DMASC to be an interesting alternative to current state-ofthe art methods that are 3D deep neural networks. Although they have not yet been applied to similar problems, their use on benchmark datasets demonstrate similar performances in terms of accuracy than those we obtain (Charles R. Qi et al. , 2017; Thomas et al. , 2019).
3DMASC We also introduce new measures of the reflective behaviour in 3D PCs, that were mostly estimated through mean intensity, and propose new inter-channel ratios to complement existing multispectral attributes  (Morsy et al. , 2017b; Wichmann et al. , 2015). Previous studies analyzing multispectral lidar faced the difficulty of linking points to their equivalents in PCs of other wavelengths, since they are never in strictly identic positions due to the sensor configuration  (Lague and Feldmann, 2020). These new ratios, along with our dual-cloud features compensate the limits of point matching, used in existing multispectral lidar analysis work  (Morsy et al. , 2017b)  when they are used on datasets with correct geometrical and radiometric calibration  (Kashani et al. , 2015; Yan et al. , 2012).
Our results also stress the superiority of multicloud classification compared to single-cloud, especially for bi-spectral lidar. We release our source code through an opensource plugin in CloudCompare (Girardeau-Montaut, 2022), hoping it will help applications of 3D remote sensing for earth observation and conservation. Although our paper illustrates specific use cases of the workflow on topo-bathymetric lidar datasets, 3DMASC can be extended to PC time series analysis, and 3D data interpretation in general. It may be applied to terrestrial laser scanning data, to SFM PCs, or even to UAV lidar sensors, which are still under development, enlarge the access to lidar surveys, but are two compact to incorporate dual-wavelength lidar sensors.
Overall Accuracy depending on the confidence threshold applied to filter the predictions. harderto optimize without expert knowledge, and require much more intensive training in terms of labelled data and computing power, while our approach relies on fewer samples. We chose to experiment on datasets containing 2000 labelled points per class, but when randomly subsampling the labelled data, we observe that high accuracies are already possible with a few hundreds of ground truth points per class, as featured in Table6. Neural networks are also more abstract and thus harder to decipher, contrary to 3DMASC thanks to feature importance and Shapley values.
Acknowledgments : This research was partially funded by the  Saur Group  and the  Region Bretagne , who the authors thank for their support. The Titan DW sensor, operated by the  Nantes-Rennes Lidar Platform  has been funded by the  Region Pays de la Loire  with funding of the RS2E-OSUNA programs and the  Region Bretagne  with support from the  European Regional Development Fund. Patrick Launeau  is greatly acknowledged for his contribution in the acquisition of the Titan DW sensor. We thank  Cyril Michon ,  Emmanuel Gouraud ,  William Gentile  from  Geofit-Expert company , and  Laurence Hubert-Moy  for their contribution in the over-all operation of the Titan DW sensor. We thank  Electricité De France  ( A. Barillier ,  A. Clutier ) for commissioning the acquisition of the Ain River survey and providing access to the data.
Compared to other classes that can be objectively defined, our separation between intermediate and high vegetation is rather subjective. The lack of various types of vegetation in the Frehel datasets prevented us to refine the vegetation class. List of classes defined for the experiments. Primary Advanced Both Ain (river) Fréhel (coast) Sand Bare ground Pebble/cobble Ground Rock Artificial ground Artificial ground Vegetated ground Vegetated ground Vegetation Intermediate High Vegetation Artificial elements Buildings Power lines Vehicles Buildings Power lines Vehicles Bathymetry Water bottom Underwater sand Underwater rock Swimming Pools / Water Water column Water column Surf zone.
Revised temperature-dependent coefficients (β) The temperature-dependent coefficients (β) were used for each PFT in MEGAN 7 (Fig. 3 ). Surprisingly, we did not find any β reported for agricultural ecosystems or grasslands. Almost all PFTs (other than boreal needle leaf deciduous trees and broadleaf deciduous shrubs) had values significantly larger than 0. 10 °C-1. This finding was particularly important for the widespread boreal needle leaf evergreen forests (βΝΕΒ = 0. 15 °C-1 ; range 0. 12-0. 19 °C-1 ) and tropical broadleaf evergreen forests (βBETr = 0. 20 °C-1 ; range 0. 14-0. 3 °C-1 ). Compared to needle leaf trees and broadleaf shrubs, broadleaf trees display higher dependencies on temperature in the release of MTs by their foliage. We note that removing the lowest 10% from our dataset had negligible impact on the revised values (Supplementary Fig.
Model simulations To comprehensively evaluate the implications of revised MT emission temperature dependencies, we performed coupled simulations employing the most widely used VOC emission model (MEGAN)  7  and a global atmospheric chemistryclimate model (EMAC)  9. The simulations were performed at 1. 8 by 1. 8-degree resolution and with hourly outputs for the original version of the model (β=0. 10 °C-1 ) and three additional scenarios for comparison. First, the MEGAN code was modified (''PFT'' simulation; Supplementary Code 1) to assign a different β for each PFT. For the PFTs that do not have any measurements, we used the value of 0. 13 °C-1 that considers the complete dataset (Fig. 3 ). Second, β was uniformly set to 0. 13 °C-1 , similar to the current approach (''ALL'' simulation). Third, β was modified based on β derived for each MT (''MTRP'' simulation).
Experimental design Searching the Web of Science, selecting results from the WOS, BCIBIOSIS, CCC, DRCI, and RSCI databases, and using the keywords ''monoterpenes'', ''emissions'', and/or ''temperature'' identified 745 peer-reviewed studies published between 1980 and 2020. Screening of these articles (page by page) decreased the number of studies to 84 that reported β derived from regression fits of experimental observations. The data for tropical plants were not sufficient to perform reliable statistical analyses, so three additional studies (which reported only data) were considered and analyzed. The timelines for the data for temperature and MT emission were extracted from published plots 6,39 using a web-based tool (WebPlotDigitizer; https://automeris. io), and data were directly obtained from Yáñez-Serrano et al.
Unit conversions for potential emission at standardized conditions (30 °C, E30). The rates of emission of monoterpenes were typically reported in units of carbon mass per gramme of foliar dry weight per hour (ng C g(dry weight) -1 h -1 ). The biogenic emission factors in MEGAN are in μg m -2 h -1 , so we used the leaf area index (LAI) and the specific leaf area (SLA) for each point to convert the reported values. SLA was obtained from the TRY database  45  (https://try-db. org, last accessed 8 June 2022). The data sets used were 1-km  46  maps scaled up from trait data measured in situ. The LAI data we used were the monthly climatological data from the ORNL DAAC database  47. The data for SLA and LAI were extracted from the above data sets, for the longitudes and latitudes of our data points while in case of missing values, the nearest neighbor with valid data was used.
Acknowledgements We acknowledge the  EMME-CARE  project from the  European Union's Horizon 2020 Research and  Innovation Programme  (grant agreement No. 856612 ), as well as matching cofunding by the  Government of the Republic of Cyprus. We acknowledge the use of the  HPC cluster Aether at the University of Bremen , financed by  DFG  within the scope of the  Excellence Initiative. We acknowledge the support of  BmBf  project  ATTO  ( 01LK1602B ).
46. 0, 170 mapping RNA sequences to an index created from de novo transcriptomes  (Bray et al. , 2016). Exploration of differentially expressed genes (DEGs) was performed with the DESeq2 v1. 34. 0 R package  (Love et al. , 2014). For each species, DEGs were obtained from the following comparisons: T1 vs. C, T2 vs. C, T3 vs. C, T2 vs. T1, T3 vs. T1, and T3 vs. T2. Transcripts with an adjusted p < 0. 05 and log2 fold change (FC) > 2 or < -2 were considered significantly differentially expressed genes. Functional annotation of the genes was performed with 175 eggNOG-mapper v2. 1. 10 against the eggNOG database v. 5. 0. 2  (Huerta-Cepas et al. , 2017 & 2019). To ensure https://doi. org/10. 5194/egusphere-2023-1875 Preprint. Discussion started: 26 September 2023 c Author(s) 2023. CC BY 4. 0 License.
they were properly annotated, annotation was also performed with TransDecoder v5. 5. 0 to predict coding sequences  (Haas and Papanicoualo, 2015) , which were aligned against a Pfam profile database v35. 0  (Mistry et al. , 2021)  using the HMMER v3. 3 alignment tool  (Finn et al. , 2011). Gene Ontology (Gene Ontology Consortium, 2015) terms were then retrieved from the pfam2go database (https://pypi. org/project/pfam2go/) 180 and functional enrichment was performed with Ontologizer v2. 1 to obtain statistically significant GOs from the DEGs of each comparison performed previously  (Bauer et al. , 2008). Functional enrichment results were summarized as tree plots and scatter plots using REVIGO v1. 8. 1  (Supek et al. , 2011). Investigation of the specific functions of DEGs was carried out by manually checking the involvement of Pfam domains and EggNOG annotations on the SMART database v9.
Acknowledgment We are grateful to the staff of the Alfred Wegener Institute (AWI), Institut polaire français Paul Emile Victor (IPEV), and Kings bay for field assistance. We thank Cátia Monteiro for her advice on RNA extraction and Erwan Corre for his expertise and guidance to process transcriptomic data. Thanks are also due to Inka Bartsch, Kai Bischof, and Simon Jungblut for their input, which improved the design and interpretation of this study, 420 and Nathalie Leblond for her help with the CHN analysis. This study was conducted in the frame of the project   660.
FACE-IT (The Future of Arctic Coastal Ecosystems -Identifying Transitions in Fjord Systems and Adjacent Coastal Areas). FACE-IT has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 869154. We also acknowledge the support of IPEV (project ARCTOS 1248) and the Prince Albert II of Monaco Foundation (project ORCA n°3051). 425 Competing interest https://doi. org/10. 5194/egusphere-2023-1875 Preprint. Discussion started: 26 September 2023 c Author(s) 2023. CC BY 4. 0 License. At least one of the (co-)authors is a member of the editorial board of Biogeosciences.
X-ray photoelectron spectroscopy (XPS) data was acquired using an ESCALAB Xi, Thermo Scientific, with a mono-chromated Al K alpha X-ray source and pass energy of 100 eV for survey scans or 20 eV for region scans. Raman spectra were recorded on a Renishaw InVia 2 Raman spectrometer at room temperature with a 532-nm laser excitation. Fouriertransform infrared (FTIR) spectra were recorded on a Spectrum 100/ Spotlight 400 spectrometer in the ATR mode. Contact angle measurements were carried out on a Ramé-Hart 200-F1 goniometer at room temperature by using about 4-μL droplets of deionized water. The solid-state NMR measurements were carried out using a Bruker Neo 300 instrument with a 7 Tesla superconducting magnet operating at frequencies of 300 MHz and 282 MHz for the 1 H and 19 F nuclei, respectively.
Approximately 2-3 mg of sample was center packed in a mm zirconia rotor and spun to 13 kHz at the magic angle. The spectra were acquired with a spin-echo sequence with a 3. 57 µs 90 • pulse length to suppress the background signal. Up to 16 k signal transients with 5 s recycle delays were co-added for a sufficient signal to noise. The spectra were deconvoluted using the Dmfit software  [34]. The spectra were referenced using the 19 F signal of PTFE at -122. 7 ppm as a secondary reference.
Acknowledgment S. C. and D. V. acknowledge the funding from the  Australian Research Council  ( DP230100542 ),  AMP Foundation's Tomorrow Fund , and the support from the  University of Newcastle. M. D. and M. C. acknowledge I-Site CAP2025,  Université Clermont Auvergne , and  Clermont Auvergne Métropole  for support via the  Académie   CAP20-25. D. J. K. acknowledge the funding from  BioHumaNetics  and support from the  School of Chemistry and Faculty of Sciences at the University of New South Wales. The authors thank the  Mark Wainwright Analytical Centre  at the  University of New South Wales.
We demonstrate that the inclusion of weather-related variables leads to more accurate default and prepayment predictions and find that climate change can increase default risk, particularly in flood-prone areas. Mortgages are of particular concern in the context of climate-related financial risks because their collateral, composed of immovable assets, is fully exposed to physical risks. Florida is a relevant case study as it has been visited by some of the most destructive and devastating hurricanes on record in the United States causing over $450 billion in damage since the early 20th century and because the value of property in Florida insured against windstorm damage is the highest in the country and on the rise  (Malmstadt, Scheitlin, & Elsner, 2009). Our analysis focuses on a portfolio constructed by extracting origination and performance data from Moody's Analytics mortgage dataset.
This dataset offers an overview of the non-agency U. S. mortgage market, which corresponds to loans within private-label securitizations (PLS). 1 New originations of these PLS largely vanished in 2008; however, in recent years, they have begun to regain a small share of new first-lien originations, growing to 4. 8% in Q1 of 2022 (Urban Institute Housing Finance Policy Center, 2022). As for weather-related covariates, we focus on tropical cyclones and the associated flood risks because these are the most quantitatively relevant hazards for the United States. For example, they represent over 50% of damages for the period 1980-2020 in the 'billion-dollar events' database of the NOAA  (Smith & Katz, 2013). Furthermore, the increasing exposure to flood risks due to climate change  (Hinkel et al.
Moody's analytics mortgage data The Moody's mortgage dataset consists of several large tables providing an overview of the non-agency mortgage market in the U. S. The full dataset contains origination details for more than 35 million mortgages; however, this total is reduced to roughly 24 million loans when requiring that the loans have a recorded five-digit ZIP code. In addition to loan characteristics, the dataset also contains monthly updates to track performance at the loan level, as well as deallevel information regarding the mortgage-backed securities (MBS) that contain these loans. We start with the set of mortgages that originated in 1990 or later and are labeled as active in January 2010. This avoids capturing the surge in defaults associated with the 2008 recession. We then examine the performance of those loans over the 2010-2019 period.
Mortgage outcomes The mortgage outcomes fall under one of three categories: loan performance, prepayment, or default. Since our sample period spans less than 10 years, many loans remain current throughout the entirety of the period. Possible early termination options include default and prepayment. As alluded to above, we define 𝑑𝑒𝑓 𝑎𝑢𝑙𝑡 in this study as a loan that is observed as 90+ days delinquent or worse, which follows the guidelines set forth by the Basel Committee on Banking Supervision (2017). We construct a binary variable following the rule: 𝑑𝑒𝑓 𝑎𝑢𝑙𝑡 𝑖,𝑡 = { 1 if borrower 𝑖 is 90+ days delinquent at time 𝑡 0 otherwise (1) This classification of 90+ days delinquent is derived from a variable named 𝑚𝑏𝑎𝑑𝑒𝑙𝑖𝑛𝑞𝑠𝑡𝑎𝑡𝑢𝑠 in the Moody's dataset, which includes several categories for a distressed loan as it moves through various delinquency stages, and out to foreclosure, REO (real estate owned), and liquidation.
Predictor variables From the Moody's dataset, we retain several predictor variables for our models. We use the included dynamic LTV ratios in the Moody's dataset to measure a borrower's home equity and capture strategic incentives to default which could arise when the value of the house is worth less than the mortgage. In addition to this, the data includes the borrower's FICO score at origination, as well as several characteristics about the loan/property. These include classifications for the property's occupancy type, number of units, loan purpose, loan product, whether there is an active prepayment penalty, and the lead manager for the MBS deal containing the loan. Tables  11  and 12  in the online Appendix provide some summary statistics for these explanatory variables. This set of loan-month observations from Moody's data is then merged with macroeconomic variables to control for general economic factors impacting the mortgage outcomes, in line with the literature  (Bellotti & Crook, 2009).
Weather events The key novelty of our approach is to integrate weather-related variables to account for the impact of extreme weather events and of their spatio-temporal characteristics on mortgage risk. In the Florida case, tropical cyclones are by far the most relevant hazards  (Smith & Katz, 2013). They can impact properties directly through wind damage, indirectly through flooding caused by heavy rainfalls or storm surges, and through a combination of factors  (Wahl, Jain, Bender, Meyers, & Luther, 2015). To account for these potential impacts, we take into consideration the spatio-temporal characteristics of tropical cyclones and heavy rainfall episodes at a high level of geographical granularity. As regards tropical cyclones, the data source is the second-generation North Atlantic hurricane database (HURDAT2; Landsea & Franklin, 2013) of the National Hurricane Center.
The database covers the period 1851 to 2019. It includes information on the name, date, hour (typically a record every six hours), and typology of the event, its geographical coordinates, and its maximum wind speed (in knots). However, it does not provide information on wind speed at the five-digit ZIP code level. We estimate this information by applying the wind speed model proposed by  Willoughby, Darling, and Rahn (2006)  and implemented in the stormwindmodel R package. 10  The model allows estimating the maximum sustained winds (in knots) for each five-digit ZIP code considering the tropical cyclone's track. Fig. 2  shows a graphical representation of the estimation for Hurricane Michael's case in 2018. Hurricane Michael was one of the most recent major hurricanes (category three or greater) in Florida.