dataesr/scientific-paragraphs-categorization huggingface dataset:
9 Denali with 20 999 traces, M w 7. 1 Hector Mine with 10 887 traces, and M w 7. 1 Ridgecrest 2 earthquake with 10 875 traces; and M w 7. 9 Denali earthquake with the total length of the surface ruptures of 657 kilometres. events having less than 7 000 rupture traces per event. For the sake of readability of the plot in Fig. 4 , both the vertical axes were cut leaving the highest values of all the plotted categories out. The most numerous data is from the most recent events in the database. We introduce the total length of the surface ruptures as a parameter to represent the extension of surface faulting and to compare the events in the database. By total length of the surface ruptures we mean here the sum of the lengths of the rupture traces of any ranking category. This value should not be confused with the commonly used parameter of surface rupture length (SRL), which refers to the end-to-end length of the principal fault of the event, usually measured without considering the fault trace complexities.
Total length of the ruptures varies largely from event to event, but naturally greater total length corresponds with the higher magnitude of the event (Fig. 5 ). In addition, we notice that for the events of same magnitude, total rupture length increases with the year of occurrence, as a clear signal of greater precision in the co-seismic surveys. For comparing the datasets of the events in the database, we prefer the total length over the number of traces, as the latter depends largely on the continuity of the trace mapping and the level of detail at which the shapefile of the rupture was constructed. Fault ranking reveals the differences within distributed rupturing. Analysis of distributed rupturing according to their ranking reveals differences between the rupturing types (Figs. 6  and 7 ).
On a case-by-case basis, in average rank 1 PF ruptures represent roughly 70% of the measurement point data, and about 60% of the total surface ruptures length. Most of the cases set between 50 and 90%, roughly, in measurement points, and between 40 and 80% in total rupture length, but there are some cases in which PF represents only one fifth of the total, or close to 100% of the data. As the PF is defined as the most continuous surface expression, it is not surprising that the share of the PF length is in average only a bit over a third of the total number of ruptures by the case. Rank 2 simple DR is the most common type of distributed rupturing in the database. However, not in all cases these ruptures have related slip measurement data. In average, rank 2 DRs represent almost one fourth of the total length of the ruptures, and about 15% of the measurement points.
Thus, this ranking category represents rupturing of high number of occurrence but large discontinuity, and small, often even immeasurable displacements (especially in the strike-slip environment). There are 15 cases in total, in which beside the rank 1, only rank 2 type of DR was recognized. Most of these cases represent the smallest class of magnitude, but there are also some larger earthquakes where no complex types of DRs were identified, probably also due to lacks in field observations. For example, 1946 M w 7. 3 Ancash earthquake is very likely to be under-sampled especially for its distributed rupturing. The 1988 M w 6. 8 Spitak earthquake present also relatively little surface rupturing data considering the size of the earthquake. Fig. 5  The events in the SURE 2. 0 database according to their date of occurrence (x-axis) and magnitude of the earthquake (y-axis).
Size of circle is proportional to the total length of the surface rupture traces in the database (all ranking categories included). Notice, that in 1987 there were two earthquakes of M w 6. 5: Edgecumbe with cumulative ruptures length of 22. 5 km, and Superstition Hills with cumulative ruptures length of 85. 1 kilometres, which are plotted here one on top of another. Also, 1999 earthquakes of Izmit and Chi-Chi are both of M w 7. 6, with cumulative ruptures lengths of 132. 2 and 126. 1 kilometres, respectively. Primary distributed rupturing (rank 1. 5) has been observed in 30 earthquakes, of which the smallest of their magnitude are 2014 M w 6. 0 Napa and 2016 M w 6. 0 Amatrice earthquakes. By definition, appearance of this kind of surface rupturing depends on the existing fault structures and its recognition depends mainly on the availability of detailed geological data and accurate topographic maps.
Bending-moment rupturing (rank 21) is a very local phenomenon that was detected only in three events of reverse kinematics in the current database: 1971 M w 6. 7 San Fernando, 1980 M w 7. 1 El Asnam, and 2005 M w 7. 6 Kashmir earthquakes. Large-scale co-seismic folding causes numerous breaks on the anticline, thus in events where bending-moment distributed rupturing is observed, the number of these ruptures represent a significant share of all the distributed rupturing traces of that event (from 30 to 71%), and a remarkable part of the total length of the ruptures by their ranking (from 40 to 64%). These areas are also very well studied in terms of slip measurements, and there are numerous rank 21 measurement points in all these cases. Of the events in the current database, flexural-slip distributed rupturing (rank 22) was observed only in the 1980 El Asnam earthquake.
Usage Notes Putting together data from various sources has not always been easy due to various reporting standards used in the field observations. For the sake of reliability, only the data we could convert to SURE standard with a minimal uncertainty were included, and in some cases some additional information might have been left out. Since the first-hand surveyors better know what type of measurements were done, we invite the scientific community to add and update the data in the database. Also, data from new events is warmly welcome. New and updated cases can be suggested in the repository, and after a revision by the Authors they will be integrated to the merged database in its next update.
Adding new data. For contributing to the database by adding data of new events we invite the contributors to familiarize themselves with the slip observations database structure (Supplementary Table  1 ), and to notice the minimum requisites that should be filled in. This includes familiarizing with the ranking scheme, in order to provide the compiler ranking. Slip observation data is filled in an empty file (SURE2. 0_Slip_Obs_empty. xlsx) that can be found from Zenodo repository, and is to be saved as "IdE_EventName_Slip_Obs. xls", IdE indicating Fig. 6  The number of the data points in SURE 2. 0 database according to their ranking. The vertical axis is cut at 540. The markers above the cut are accompanied with a label of the number of the points of the ranking category in question; the markers above the cut are not plotted in scale.
Reviewing existing data. In case of updating the data already in the merged database, we invite the contributors to copy the data of the earthquake to be updated, to make the updates and prepare separate flat file for slip observations and corresponding shapefile following the steps described for new cases. The files are saved to the folder of the cases to be reviewed, and update to the existing cases will be done as new cases are integrated to the merged database. In case of reviewing original data or suggesting update to its geometry, a compiler is asked to create a file of "IdE_Notes. txt" and leave short description regarding the updates done.
Izu Peninsula Bay, 1978 Izu Oshima offshore, 1984 Nagano Pref. West, 1998 Iwate Pref. , 2000 Tottori Pref. , 2004 Niigata Pref. , and 2008 Iwate-Miyagi earthquakes are events included in the original dataset 2 but are not yet in SURE 2. 0 format. New events have been added in SURE 2. 0, such as the 1946 Ancash, 1950 Fort Sage mountains, 1970 Calingiri, 1975 Oroville, 1978 Thessaloniki, 1980 El.
Acknowledgements This database is compiled under agreement on collaboration between IRSN,  INGV ,  ISPRA  and  University of Chieti-Pescara , and was initially encouraged by an  INQUA  project (SURFACE). The authors are grateful to all those who have contributed to discussions that have led to development and implementation of the unified database of surface rupturing. We would like to express our gratitude also to the  Fault Displacement Hazard Initiative working group  (https://www. risksciences. ucla. edu/nhr3/fdhi/home)for fruitful collaboration.
Izu Peninsula Bay, 1978 Izu Oshima offshore, 1984 Nagano Pref. West, 1998 Iwate Pref. , 2000 Tottori Pref. , 2004 Niigata Pref. , and 2008 Iwate-Miyagi earthquakes are events included in the original dataset 2 but are not yet in SURE 2. 0 format. New events have been added in SURE 2. 0, such as the 1946 Ancash, 1950 Fort Sage mountains, 1970 Calingiri, 1975 Oroville, 1978 Thessaloniki, 1980 El Asnam, 1981 Pisia, 1981 Corinth, 1983 Coalinga (Nuñez), 1983 Borah Peak, 1986 Marryat Creek, 1988 Tennant Creek, 1988 Spitak, 1993 Killari, 1999 Chi-Chi, 2005 Kashmir, 2008 Wenchuan, 2012 Pukatja, 2016 Petermann, 2016 Amatrice, 2016 Parina, 2019 Ridgecrest I and II, and 2019 Le Teil earthquakes.
An influx of spectral retrieval models that solve the inverse problem has left modellers spoilt for choice. Retrieval codes include: TauRex 3 (Al-Refaie et al. 2021), NEMESIS  (Irwin et al. 2008) , CHIMERA  (Line et al. 2013) , ARCiS  (Ormel & Min 2019; Min et al. 2020) , BART  (Harrington 2016) , petitRADTRANS  (Mollière et al. 2019) , HELIOS  (Kitzmann et al. 2019; Lavie et al. 2017) ,  POSEIDON (MacDonald & Madhusudhan 2017) , HyDRA  (Gandhi & Madhusudhan 2018) , SCARLET  (Benneke 2015) , PLATON II  (Zhang et al. 2019)  and  Pyrat-Bay (Cubillos & Blecic 2021). Each retrieval code attempts to infer the abundance profiles of the atmospheric species. Sampling molecular abundances at each layer of the atmosphere exponentially inflates the sampling dimension to hundreds of parameters for each molecule.
Sources of the ln K i include the NIST-JANAF  (Chase 1986 ) database but must be fitted as a function of temperature before they can be used. Various fitting functions have been proposed in the literature including the form by  Tsuji (1973) : ln K i = a 0 + a 1 θ + a 2 θ 2 + a 3 θ 3 + a 4 θ 4 (2) where a n are fitting coefficients and θ = 5040 T where T is temperature in Kelvin. Another form used are the NASA polynomials formalism given by  McBride et al. (1993) : H i = a 0 + a 1 T 2 + a 2 T 2 3 + a 3 T 3 4 + a 4 T 4 5 + a 5 T S i = a 0 ln T + a 1 T + a 2 T 2 2 + a 3 T 3 3 + a 4 T 4 4 + a 6 ln K i = H i -S i (3) where H i and S i are the enthalpy and entropy for species i respectively. To summarise, building a chemical equilibrium code will require three choices: (1) the choice in an algorithm for minimizing the Gibbs free energy.
TauREx 3. 1 (Appendix A) remedies this issue through the introduction of a plugin system. This system allows for light packages that can be built, distributed through PyPI and installed. These packages are then detected by TauREx and seamlessly 'plugged in' to the framework (see Appendix B for more details). Plugins can extend and enhance every aspect of the framework, including but not limited to: GPU-acceleration (Appendix B. 1), forward models, opacities, prior functions, samplers and importantly, chemical models (Appendix C). We exploit TauREx 3. 1 and its plugin feature to compare and cross-validate three chemical codes: ACE  (Agúndez et al. 2012 (Agúndez et al. , 2020)) , Fastchem  (Stock et al. 2018 ) and GGchem  (Woitke, P. et al. 2018)  under the TauREx retrieval framework.
(2012)  and  Venot et al. (2020). Many of the reactions and species in the respective networks derive a portion of their rates from combustion mechanisms which deal with C, H, N and O bearing molecules in temperatures up to 3000 K and pressures up to 100 bar. Such conditions are similar to those found in Ultra Hot Jupiters; ACE provides almost double the number of C/H/O/N bearing species compared to other thermochemical datasets such as  Chase (1986).
Fastchem FastChem  (Stock et al. 2018 ) is a C++ chemical equilibrium code from the Exoclimes simulation platform that implements a new semi-analytical method to minimize the Gibbs energy. It decomposes the system into a set of singular variable coupled nonlinear equations. FastChem also exploits quadruple precision and the Nelder-Mead optimization algorithm to converge quickly at temperatures as low as 100 K. Thermochemical data for the 396 neutral and 114 charge species is sourced from the NIST-JANAF database  (Chase 1986 ) and fit the expression: ln K = a 0 T + a 1 ln T + a 2 + a 3 T + a 4 T 2 (4) FastChem is the main chemical model used in the HELIOS  (Kitzmann et al. 2019; Lavie et al. 2017 ) radiative transfer code and has seen successful use in retrievals of brown dwarf companions HD 1160B and HD 19467B  (Mesa et al.
2020)  and secondary eclipses of WASP-121b  (Bourrier et al. 2020)  2. 1. 3. GGchem GGchem (Gleich-Gewichts-Chemie)  (Woitke, P. et al. 2018 ) is a FORTRAN-90 rewrite of the code that was originally used to study condensation processes in late M-type stars. The rewrite updates the iteration scheme extends numerical accuracy to quadruple precision, improves the range of applicability down to 100 K, and includes equilibrium condensation. GGchem supports the elements from hydrogen to zirconium plus tungsten and includes ion chemistry. GGchem supports the widest range of fitting function which include forms given by  Gail & Sedlmayr (1986) ,  Sharp & Huebner (1990)   and Equations 2, 3  and 4. GGchem can exploit multiple sources of thermochemical data. GGchem will continuously add new species from each file; any species already defined will be overwritten with the newer data.
Forward Model Comparison For each chemical equilibrium code we model a HD-209458 b like planet as our test system with stellar and planetary parameters from  Stassun et al. (2017)  given in Table  1. The atmosphere is modelled as a cloud-free isothermal transit spectrum that includes molecular absorptions, collisionally induced absorptions (CIA) from H 2 -H 2 and H 2 -He and Rayleigh scattering calculated for all molecules given by  Cox (2015). We then simulate observed spectra as recorded with the JWST NIRISS instrument with GR700XD grism and the NIRSPEC instrument with an G395M/F290LP filter. We generated the uncertainties using taurex_jwst plugin, which uses the Exowebb library as its noise model backend. For Fastchem and GGchem, we model two cases: The C/H/O/N case where we limit the available elements to H, He, C, N, and O to allow for direct comparison with the ACE chemistry model.
The cases where the chemical code elemental composition and processes match the input spectrum will assess the underlying biases associated with each codes methodology, thermochemical data source and equilibrium constant function choice. Conversely, the cases where the elemental compositions and processes do not match will assess the biases associated with our initial assumption of the atmosphere's chemistry. Our retrieval setup utilizes TauREx-CUDA contributions (Section B. 1) to accelerate the forward modeling computation and the MultiNest Bayesian nested sampling code  (Feroz et al. 2009; Buchner et al. 2014)  with Message Passing Interface (MPI). The nested sampling utilizes 750 live points, with an evidence tolerance of 0. 5 and uniform priors given by Table  3. For the free retrieval, we use priors given by Table  4  and derive Z and C/O after the retrieval through post-processing  (Lee et al.
Uniform priors used by the free chemistry retrieval. complete. The calculation of equilibrium condensation on the CPU is a significant proportion of the forward modeling time; therefore, GPU acceleration of the optical depth is of little benefit in this particular case. 5. Average retrieval times for each chemical code and species used. Benchmarks were conducted on the OzSTAR Supercomputing @ Swinbourne cluster on 5 nodes; Each node has 2 Intel Gold 6140 18-core processors and two nVidia P100 12GB PCIe GPUs. For all the plots, we will maintain the same color scheme, i. e. blue for ACE, red for Fastchem, green for GGchem, and orange for GGchem with condensation.
Heavy input spectrum We examine the case where the retrievals fit against a heavy species spectrum. When examining only FastChem and GGChem, the posteriors in Figure  9  display a high degree of overlap and shape similarity. Both achieve almost the same correlations between parameters and tightly constrain their posteriors. Looking at the retrieval in its full context, the posteriors in Figure  10  include both ACE and GGchem with heavy condensation, and we see a significant deviation in their resultant posteriors. The resultant molecular profiles in Figure  11  and spectra in Figure  12  reveal the different approaches both ACE and GGchem with condensation take to reach Each equilibrium chemical code utilizes the heavy element list (except ACE and free) and is fit against a JWST simulated transit spectra of HD 209458b using parameters given by Table  1  and abundances computed using ACE  (Agúndez et al.
FastChem GGchem Figure  9. Retrieval posteriors FastChem (red), GGChem (green) only. Each chemical code utilizes the heavy element list and is fit against a JWST simulated transit spectra of HD 209458b using parameters given by Table  1  and abundances computed using FastChem  (Stock et al. 2018 ) with heavy species. Priors used are given by Table  3. Figure  13  as it is increasing the CH 4 abundance to raise the overall baseline spectrum. However, the lower TiO and VO signature results in the temperature dropping only to 1350 K compared to the 1120 K in the previous case and the reduction in metallicity only to 0. 7 Z. Similarly, the heavy models mask TiO and VO by raising the water abundance, as seen in Figure  14. Since there is still TiO and VO present, the temperature difference from truth is only 60 K compared to the 150 K drop in the C/H/O/N case.
The results of the free retrieval on the heavy with condensation case are similar to the one from the heavy case, except for TiO and VO. Due to condensation, TiO and VO are sequestered from the bottom of the atmosphere, which leads the free constant retrieval to average these abundances. We believe a. Posteriors of ACE (blue), FastChem (red), GGChem (green), GGChem with condensation (orange) and free chemistry (purple). Each equilibrium chemical code utilizes the heavy element list (except ACE and free) and is fit against a JWST simulated transit spectra of HD 209458b using parameters given by Table  1  and abundances computed using FastChem  (Stock et al. 2018 ) with heavy species. Priors used are given by Table  3. more complex parametric description of those profiles, such as the one presented in  Changeat et al.
(2019) , would avoid those biases. Due to the biases arising from the constant assumption on TiO and VO, the H 2 O, CO and CO 2 are more abundant in the atmosphere, causing a higher metallicity in the posteriors. Overall, the final spectra in Figure  15  show that the C/H/O/N and heavy case agree qualitatively better than their respective counterpart retrievals (see Figure  13 ). Retrieved spectra for ACE (blue), FastChem (red), GGChem (green), GGChem with condensation (orange) and free chemistry (purple). Each equilibrium chemical code utilizes the heavy element list (except ACE and free) and is retrieved against simulated JWST spectra (black) computed with FastChem with heavy chemistry using parameters from Table  1. Spectra have been binned to a lower resolution and offset for clarity. When the model species and input spectrum species do not match, we find a significant deviation between true and retrieved values.
These retrievals demonstrate that the assumptions we make on the composition and processes (i. e. choice of elements, condensation etc. ) occurring in the atmosphere are the significant source of bias. Unfortunately, we have no prior knowledge of the underlying physics within exoplanetary  Each equilibrium chemical code utilizes the heavy element list (except ACE and free) and is fit against a JWST simulated transit spectra of HD 209458b using parameters given by Table  1  and abundances computed using GGChem  (Stock et al. 2018 ) with heavy species and condensation. Priors used are given by Table  3. atmospheres in a realistic setting, so immediately assuming a self-consistent model can lead to confident and incorrect conclusions of atmospheric composition. In the context of the next generation, extensive surveys (Ariel:  Tinetti et al.
careful exploration of the information content of the spectra and identification of species and processes. Following this approach would allow us to make an informed constraint on priors when introducing more complexity and self-consistency. Next-generation retrievals must have the flexibility to include a wide range of processes and increase/decrease its model complexity to interpret spectra successfully. Retrieved spectra for ACE (blue), FastChem (red), GGChem (green), GGChem with condensation (orange) and free chemistry (purple). Each equilibrium chemical code utilizes the heavy element list (except ACE and free) and is retrieved against simulated JWST spectra (black) computed with GGChem with heavy elements and condensation using parameters from Table  1. Spectra have been binned to a lower resolution and offset for clarity We have presented here one of the first systematic comparisons of equilibrium chemical codes in the context of exoplanet retrievals.
This comparison was made possible by the newest version of TauREx 3. 1, allowing each chemical code to be 'plugged-in' to the framework. TauREx 3. 1 and its available plugins are available on PyPI as source distributions and binaries for Windows, macOS and Linux. The sources for TauREX 3. 1 and its plugins are also available on the ucl-exoplanet GitHub 1  with permissible licenses on most plugins. We hope that providing a framework with a plugin interface will allow for more collaboration and interconnectivity between different fields and future comparative work. Regarding the chemical comparisons, we demonstrate that ACE, GGchem and FastChem reach the same conclusions given the same assumptions. The implementation, source of thermochemical data and fitting of equilibrium functions introduce little to no bias in retrievals.
B. PLUGINS A key feature in the first release of TauREx 3 allowed the user to inject their code into the retrieval pipeline from the input file. This feature allowed for the inclusion of new atmospheric models and parameters in the retrievals without extensive knowledge of the underlying systems within TauREx 3. However, these codes are considered exceptions from the standard TauREx 3 pipeline and required specific input keywords and files in order to function. This way also prevents the accumulation of enhancements to the framework provided from the feature as they must now obtain a specific code file. The distribution of such custom enhancements was cumbersome, especially for FORTRAN and C++ codes, as they would require additional manual stages before they could be used. In most cases, developers directly placed their code into their copy of the TauREx 3 codebase.
This approach, while valid, runs the risk of splintering TauREx 3 into multiple, incompatible versions. The latest version of TauREx 3 (3. 1+) remedies this problem through the use of plugins. Plugins are, in essence, installable enhancements to TauREx 3. They allow new features to be included in the pipeline natively without modifying the main TauREx 3 codebase. A plugin author can build new chemistries, profiles, models and optimizers and distribute them to other users through the Python packaging system. To give an example, when TauREx 3. 1 is installed (or upgraded from version 3. 0), only free chemistry is available. An arbitrary input file for that may look like this: [Chemistry] chemistry_type = free fill_gases = H2,He ratio = 0. 17 No other chemical model is available. However, if an equilibrium model such as GGChem  (Woitke, P.