dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2015). However, this suggestion has not been tested. Furthermore, the potential degree of multi-fault connectivity partly depends on the subsurface geometry of the Southern San Cayetano fault (SSCF) (Fig. 1 ) and the nature of its potential connection with the neighboring San Cayetano, Pitas Point, and Ventura faults, which remains unresolved  (Hubbard et al. , 2014; Hughes et al. , 2018; Levy et al. , 2019). In this study, we examine petroleum industry well-log data and existing structural cross sections developed from well-log data, in conjunction with geologic maps, to produce a series of cross sections across the northern boundary of the central Ventura basin and to characterize the three-dimensional geometry of the low-angle SSCF. Using these data, we construct a three-dimensional model of the lowangle SSCF and compare the three-dimensional model to previous interpretations of the SSCF derived from structural modeling.
We then use this complex thrust fault geometry in static Coulomb stress modeling using an updated method for creating variable-strike faults in Coulomb 3. 4  (Mildon et al. , 2016). The code was updated here to incorporate faults that change geometry both down-dip and alongstrike i. e. , to be able to model inferred ramp-flat fault geometry. The static Coulomb stress modeling explores whether subsequent earthquakes on the low-angle SSCF and the San Cayetano fault may be promoted by static Coulomb stress changes as a result of modelled ruptures on the Pitas Point or Ventura faults. The models also provide a framework for how simple static Coulomb stress modeling can be used in fold and thrust belts to forecast potential fault behavior in the absence of recent historical earthquake data.
Datasets We integrated various surface and subsurface datasets to produce a series of structural cross sections along strike of the SSCF, to generate a three-dimensional model of the SSCF, and to investigate how the SSCF may connect with the San Cayetano and Ventura faults. The full set of data sources, including both well data and geological maps, is as follows:  California Department of Conservation (Division of Oil, Gas and Geothermal Resources or DOGGR) online well database: A comprehensive online record of almost all historical oil wells drilled in California including, but not limited to, oil and gas production records, resistivity logs, drillers logs, paleontological data, and dipmeter data (herein referred to as 'the DOGGR online well database').  Ventura Basin Study Group well correlation sections: The Ventura Basin Study Group (VBSG) provide a comprehensive review of the subsurface structure of the onshore Ventura basin comprising structural contour maps of key stratigraphic horizons and well-correlation sections across all major structures based on an integrated analysis of well data from 1200 petroleum industry wells  (Hopps et al.
, 1992). Wells are typically tied or correlated in four directions, to create a grid of correlation sections that allows for a unified three-dimensional interpretation of subsurface structure and stratigraphy. Primary data extracted from the VBSG well correlation sections to characterize the SSCF included dipmeter data, dip data from core-logs, and resistivity data.  Existing structural cross sections: In addition to the VBSG, several researchers have previously examined the subsurface structure of the study area and produced structural cross sections  (Schlueter, 1976; Çemen, 1977; Huftile, 1988; Rockwell, 1988; Çemen, 1989). Cross sections and interpretations of well data included on the cross sections (if present) were re-interpreted to characterize the SSCF.  Geological maps: Numerous geologic maps have been drafted in the study area  (Dibblee, 1987; Dibblee, 1990a; Dibblee, 1990b; Dibblee and Ehrenspeck, 1992; Tan et al.
, 2004; Campbell et al. , 2014). Data on outcrop patterns, mapped folds and faults, along with strike and dip data were extracted from maps and used to aid cross section construction.  Geomorphic maps: A sequence of uplifted alluvial terraces and offset alluvial fans have been mapped in the hanging wall of the SSCF and in the Upper Ojai Valley  (Rockwell, 1983; Rockwell, 1988; DeVecchio et al. , 2012; Hughes et al. , 2018). These data helped quantify the surface expression of the SSCF to compare with results of the subsurface investigation.
Dipmeter data With the onset of activity on the SSCF thought to have occurred within the last 58 kyr and with an inferred maximum surface slip rate of ~1. 9 mm yr -1  (Hughes et al. , 2018) , the SSCF is expected to have accumulated a maximum displacement of ~110 m. Given such limited stratigraphic offset, methods of fault identification that rely solely on unambiguous identification of stratigraphic offsets in the subsurface (e. g. , biostratigraphy or repetition of stratigraphy) may not be the most effective methods to identify the SSCF in well data. Disturbances in dipmeter trends are often associated with faulting and we interpret an abrupt change in dip, which can either be an abrupt decrease or increase in dip-angle with depth (or both if one follows the other), to represent potential evidence for a fault cut (where the ©2020 American Geophysical Union.
, 1992). Original, uninterpreted dipmeter data are only sporadically available in the DOGGR online well database within the study area, so we rely mainly on dip data included in existing correlation and structural cross sections that have been corrected for well drift from vertical and adjusted for the orientation of the cross section data panel (e. g. ,  Schlueter, 1976; Huftile, 1988; Hopps et al. , 1992).
Core logs Many well records within the DOGGR online well database contain core logs that briefly describe the stratigraphy and note any specific features such as faults, fractures, gouge zones, slickensides, fossils, or traces of hydrocarbons. Core logs also contain dip data annotations ascertained from the orientation of bedding, sedimentary structures, or fractures within the core. Such annotated dip data from core logs are typically low resolution compared to the almost continuous readings from dipmeter data. However, changes in core-log dips were used to either support interpretations from dipmeter data or in place of dipmeter data if dipmeter data were not available. Core logs were also examined for any first-hand observations of faulting, such as pervasive fracture zones or gouge zones.
Resistivity data Resistivity data are available for many wells in the study area from the DOGGR online well database. Fluid migration along the fault plane can affect the resistivity signal, while fault gouge and/or fractures ©2020 American Geophysical Union. All rights reserved. surrounding a fault zone can potentially cause the logging tool to fail  (Hubbard et al. , 2014). However, resistivity data alone from a single well are not normally used to suggest the presence of a fault  (Devilliers and Werner, 1990). Sharp variations in resistivity have previously been used to support the identification of faults when they co-exist with other data that suggest the presence of a fault, for example dipmeter data  (Devilliers and Werner, 1990). Changes in resistivity data, when they are observed at a depth roughly equal to the depth with abrupt variations in dip, were used to provide further supporting evidence for fault cuts related to the SSCF.
Cross sections and three-dimensional fault model construction We created three vertical structural cross sections (Fig. 3 ) and a three-dimensional fault model for the SSCF (Fig. 4 ) to map any potential along-strike variations in the two-dimensional and threedimensional subsurface structure of the SSCF and to present our interpretation of how the SSCF intersects previously mapped structures and stratigraphy. Cross-section lines were selected to approximate the lines of section of the VBSG well-correlation sections within the study area  (Hopps et al. , 1992). The VBSG sections contain a large amount of well data corrected for well-bore deviation onto section lines. By constructing our cross sections in a similar orientation to the VBSG sections, the ©2020 American Geophysical Union. All rights reserved.
cross sections followed the structural trend of the SSCF and the distance that well data needed to be projected onto the section was kept to a maximum of ~2 km. While this method does involve some (minor) projection of well data onto the line of cross-section (Fig. 1b ), the wells used in construction of the three-dimensional model are not projected. Utilizing this method, the dip data contained in the VBSG sections could be used as a guide to model the subsurface geometry of the SSCF based on the fault cuts described in Table  1. To construct the three-dimensional fault model of the SSCF, we georeferenced geological maps containing the well locations in the seismic interpretation software Petrel TM. Fault intersections from the wells that we interpret to be related to activity on the SSCF were plotted as points and these points were gridded into a fault surface.
Methods The newly constructed three-dimensional geometry for the SSCF was used in static Coulomb stress modeling  (Harris and Simpson, 1992; King et al. , 1994; Harris, 1998; Parsons et al. , 1999; Toda et al. , 2005)  using the software Coulomb 3. 4  (Lin and Stein, 2004; Toda et al. , 2005). In our simple model, we assumed an initial stress of zero on all faults and simulated concentric ruptures on specific faults to observe patterns of modeled static Coulomb stress changes on the San Cayetano fault. No attempt was made to model dynamic stress effects or the current stress state of the faults resulting from historical ruptures (e. g. ,  Deng and Sykes, 1997a, 1997b) , interseismic loading (e. g. ,  Verdecchia and Carena, 2016; Mildon et al. , 2017; Wedmore et al. , 2017) , or cumulative transferred stress over a longer time period from post seismic relaxation of the lower crust and mantle (e.
Modeled fault surfaces Faults were reconstructed in Coulomb 3. 4  (Lin and Stein, 2004; Toda et al. , 2005)  following the methodology of  Mildon et al. (2016)  for creating non-planar source and receiver fault inputs with variable strike. The original code for creating non-planar reverse faults in Coulomb 3. 4 only facilitated the modeling faults with variable strike and did not allow for modeling faults that change their geometry down-dip, a common feature of faults in fold and thrust belts (e. g. ,  Shaw and Suppe, 1996). Accordingly, the code for creating non-planar reverse faults in Coulomb 3. 4 was updated here to ©2020 American Geophysical Union. All rights reserved. incorporate faults that change geometry both down-dip and along-strike and, therefore, to model listric or ramp-flat fault geometry.
The code is described in detail in  Mildon et al (2016)  and the updated version of the open source code is available from: https://github. com/ZoeMildon/three-dimensionalfaults/tree/Variable_dip_faults. The three-dimensional surface of the low-angle SSCF used in our model is based on the analysis of subsurface data presented in section 4. The Pitas Point, Ventura, and San Cayetano faults were reconstructed to produce a close match to fault surfaces included in CFM 5. 2 (Fig. 5a )  (Plesch et al. , 2007; Nicholson et al. , 2017a). Fault surface traces were loaded in Google Earth TM and traced over by hand. Faults were projected perpendicular to mean fault strike using a dip-profile that was based on the average dip of the fault surfaces included in CFM 5. 2 (Fig. 5b )  (Plesch et al. , 2007; Nicholson et al.
Static Coulomb stress interactions The interpretation of the SSCF presented here indicates that the SSCF is not hard linked to the Ventura fault. We note, however, that well data are limited in the area between the eastern end of the Ventura fault and the western end of the SSCF, and so data are lacking to precisely model this fault intersection. If the SSCF is not connected to the Ventura fault, the SSCF may not be as effective a pathway for through-going ruptures between the Pitas Point/Ventura and San Cayetano faults as previously suggested  (Hughes et al. , 2018). Through-going ruptures are thought to occur between the Pitas Point, Ventura, and San Cayetano faults due to large-magnitude uplift events inferred in paleoseismic studies  (Dolan and Rockwell, 2001; McAuliffe et al. , 2015; Rockwell et al.
, 2016). If such ruptures do occur, they would need to propagate between the faults at depth along blind ramp structures such as those suggested by kinematic structural modeling  (Hubbard et al, 2014; Levy et al. , 2019) , cross section balancing  (Namson and Davis, 1988; Huftile and Yeats, 1995, 1996) , and mechanical modeling based on GPS data  (Marshall et al. , 2017; Hammond et al, 2018). However, in the absence of a confirmed through-going rupture pathway identified in the well data, here we use the results of the static Coulomb stress modeling to investigate how proposed large-magnitude earthquakes on the Pitas Point and Ventura faults may influence subsequent seismicity on the San Cayetano and Southern San Cayetano faults, via static Coulomb stress transfer. Models of static Coulomb stress change have previously been utilized to understand the kinematics for the multi-fault rupture sequence during the 2010 Mw 7.
Conclusions Results from an integrated analysis of well data, geological maps, and cross sections help document the three-dimensional geometry of the low-angle Southern San Cayetano fault (SSCF) and reveal evidence ©2020 American Geophysical Union. All rights reserved. for the potential subsurface connection of the SSCF with the San Cayetano fault. The geometry of the low-angle SSCF derived from well-data differs from both of the previous models for the SSCF, which were derived from structural modeling to explain the presence of a large south-dipping fold limb in the footwall of the San Cayetano fault. Ruptures that nucleate at depth on the San Cayetano fault may sometimes propagate up-dip along the SSCF rather than the steeper upper section of the central San Cayetano fault, which potentially makes the SSCF a significant ground rupture hazard in the highly populated Santa Clara Valley.
The lines in white represent the cross sections included in Figure 3 and well projections on to the section line are represented with thin white lines. Well locations are from California Department of Conservation online well database. SPP = Santa Paula Peak, SSCF = Southern San Cayetano fault, LMA = Lion Mountain anticline, JCF = Javon Canyon fault, LCF = Lion Canyon fault, WSCF = Western San Cayetano fault, ESCF = Eastern San Cayetano fault, SAF = San Andreas Fault, TC = Timber Canyon, OC = Orcutt Canyon, SPC = Santa Paula Creek.
Acknowledgements Well data were taken from the online database provided by the  California Department of Conservation, Division of Oil, Gas , and Geothermal Resources website which is freely available at: https://giscalifornia. opendata. arcgis. com/datasets/cadoc::well-finder(last accessed January 2020). Work supported by funding from  Southern California Earthquake Center (SCEC)  award numbers  15100  (to DHR and DDV),  17184  (to DHR),  16049  (to TKR), and  17024  (to TKR). Additional funding was supplied by a  Royal Geological Society  postgraduate research grant (to AH), an  Imperial College Janet Watson bursary  (to AH), and a postgraduate research grant from the  British Society for Geomorphology  (to AH). The authors would also like to thank  Schlumberger  for providing usage of the software Petrel TM.
Key wells are enlarged relative to other wells to include the numeric identifiers for the wells in Tables1 and S1, and Figures 2, 3, and 4. The lines in white represent the cross sections included in Figure 3 and well projections on to the section line are represented with thin white lines. Well locations are from California Department of Conservation online well database. SPP = Santa Paula Peak, SSCF = Southern San Cayetano fault, LMA = Lion Mountain anticline, JCF = Javon Canyon fault, LCF = Lion Canyon fault, WSCF = Western San Cayetano fault, ESCF = Eastern San Cayetano fault, SAF = San Andreas Fault, TC = Timber Canyon, OC = Orcutt Canyon, SPC = Santa Paula Creek.
single factor linear regressions of yield against water stress, potential N balance, and pest/weed scores. In a second analysis, two classification and regression tree (CART) models  (Delmotte et al. , 2011; Tittonell et al. , 2008)  were built (R software Rpart package, Terry  Therneau and Beth Atkinson (2019) ). The first CART aimed at identifying the main factors explaining yield variability. It was built on the total dataset (n=99) with the relative yield gap as the target variable (see Table  2  for calculation). Plausible yield-limiting and yield-reducing factors were set as explanatory variables: highest weed score, maize planting density, N balance and soil type. The second CART was performed with the main factor explaining yield gap variability (identified with the first CART) as the target variable.
Acknowledgements We thank the  Department for Agricultural Land Management (DALaM)  and the  Provincial Agriculture and Forestry Organisation (PAFO)  in  Xieng Khouang  for their support and assistance. We especially thank  Bounma Leudphanane  for his invaluable assistance in fieldwork over the three years. We are grateful to the farmers in the villages of Xay, Leng and Nadou for their availability and their warm welcome. The authors are grateful to  Peter Biggins  for English language corrections. This research was funded by the  Directorate-General for Development and Cooperation -EuropeAid  ( EuropeAid/132-657/L/ACT/LA ) and the  Agence Française de Développement (Conservation Agriculture  within the  Northern Upland Development Programme, NUDP ).
e. , temperature, pressure, density) and spectral (i. e. , absorption/scattering extinction coefficient, scattering phase function) properties. These physical and optical properties are either user-defined or computed from DART atmosphere SQL database that was initially derived from MODTRAN simulations and LOWTRAN source code. The atmospheric RT modelling relies on the DOM and the spectral application of Beer's law with band mean optical properties  (Gastellu-Etchegorry et al. 2004 ). This modelling is capable to simulate the direct and diffuse irradiances at any altitude (TOA, BOA, etc. ) in the atmosphere as well as the radiative coupling with Earth surface RT modelling.
e. , Cumulus and Stratus) are stored in the DART atmosphere SQL database. They were derived from MODTRAN simulations. Users can also define any specific cloud type. This new functioning allows one or more types of clouds coexist with gases and aerosols in DART atmosphere. However, it is very inefficient to compute one-by-one constituent the multiple scattering. For that, a technique similar to  Berk et al. 1998  was applied to accelerate the multiple scattering modelling. In short, an effective single scattering albedo and an effective phase function weighted by the scattering extinction coefficient are used to simulate the multiple scattering of the combined aerosols and clouds. An aerosol or a cloud is called "non-gas element" in the combined mixture. This technique gives the same scattered radiance as the combined scattered radiance computed one-byone element while it costs much less time.
ACKNOWLEDGEMENTS This research is supported by the  TOSCA program  of  CNES  and the  Occitanie region, France. We are grateful to the  DART  team computer scientists  Dr. Nicolas Lauret  and  Jordan Guilleux  for their support. Radiation measurements at Lamasquère are mainly supported by  ICOS-France  and the  Regional Spatial Observatory (OSR)  from  CNRS ,  CNES  and  University of Toulouse. Regarding these data, we are also grateful to  Franck Granouillac  and  Bartosz Zawilski  for their technical support and  Tiphaine Tallec  and  Aurore Brut  for data processing.
The single scattering albedo ω 7 per element n is supposed to be independent of altitude. The fraction of scattering extinction coefficient  7,C and the effective single scattering albedo  ] C are computed in a later step:  7,C = ∫ M G NNO PQ (I) ∆R J ∫ M G NNO PQ (I) SI TUV O SI for aerosols and α 7,C = F G,H ∆I J • ∫ W XYZ (I) ∆R J ∫ W XYZ (I) SI TUV O SI for clouds. ^H,J •_ H,J ∑ ^H,J •_ H,J H , with ∑ 𝑝 7,C = 1 7 , and 𝜔 ] C = ∑ ^H,J •_ H,J H H ∑ ^H,J. 3) Computation of effective scattering phase function: the scattering mechanism is modelled with one effective scattering phase function for all non-gas elements: 𝑃 c C (𝜑) = ∑ 𝑝 7,C • 𝑃 7,C (𝜑) 7 1) Preliminary data processing: band mean optical properties (i. e. , vertical optical depth 𝜏 6,7 , single scattering albedo 𝜔 7 , parameters of double Henyey-Greenstein phase function) per element are computed from optical properties at 1 cm -1 spectral resolution in the DART atmosphere SQL database.
Aerosol extinction coefficient profiles (𝛼 6 ::; =. (𝑧)) at 550 nm and cloud density profiles (𝜌 @AB (𝑧)) are directly extracted from the DART atmosphere SQL database. F G,H ∆I J • 2) Computation of extinction coefficient and effective scattering albedo: DART atmosphere consists of j discrete layers, with layer thickness ∆ C per layer. The total extinction coefficient α 7,C per element n per layer j is α 7,C =.
It stresses the potential of DART to accurately simulate irradiance in cloudy days. Also, results stress that very large underestimate of irradiance in PAR domain, especially in blue domain occurs if aerosols are used to mimic clouds. Yes The inversion procedure proposed in this work can be potentially If E measured <𝜀𝜀 2 ) StepB=False used for a wide range of experiment applications if only broadband BOA irradiance is available. Indeed, direct and diffuse No Yes BOA irradiance in narrow bands are commonly required for many urban and vegetation studies (Dissegna et al. 2019). This is No typically the case for studying the sun induced chlorophyll 𝑘𝑘 𝑜𝑜 =𝑘𝑘 𝑖𝑖 fluorescence (Gastellu-Etchegorry et al. 2017). If E measured > 0 and E DART,i E measured > 2 Yes Yes No Yes No Yes E DART,O =E DART (𝑘𝑘 𝑜𝑜 ) Iteration+=1 If 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 and E DART,O =None If 𝑘𝑘 𝑜𝑜 >𝑘𝑘 𝑎𝑎,𝑚𝑚𝑎𝑎𝑚𝑚 Yes 𝑘𝑘 𝑜𝑜 , E DART,O Iteration No No If |E measured -E DART,O | E measured <Max(𝜀𝜀 1 ,𝜀𝜀 2 ) Yes No Yes Yes If |E measured -E DART,K | E measured <𝜀𝜀 1 Yes k, E DART,k No No If E DART,k >E measured Yes 𝑘𝑘 𝑖𝑖 =k, E DART,o =E DART,k k, E DART,k Iteration No Iteration+=1 If iteration > IterationMax Yes No k APPENDIXFlowchart of the inversion python script in DART distribution.
Data Acquisition EEG signals were recorded using a 256 channel Geodesic Sensor Net (Electrical Geodesics Inc. ; EGI, Eugene, OR). All channels were referenced to the vertex (Cz) and collected with a high impedance amplifier, Net Amp 300 amplifier (Electrical Geodesics) using Net Station 4. 5 software (Electrical Geodesics). Data were recorded continuously with a high-pass filter at 1 Hz and a sampling rate at 1000 Hz. Subjects were instructed to limit body movements, eye blinks, and muscular contractions during task selection and reward feedback.
Data Analysis EEG data analysis was performed using Cartool Software 3. 55  [54]. Raw EEG data were rereferenced offline to a common average reference. Analyses were conducted on the interval around the reward screen for two intervals. The first temporal interval, computed for easy and hard tasks, was related to the SPN. Epochs of 700 ms (500 ms prior to the outcome to 200 ms after) were extracted from the raw data, with a baseline correction of 100 ms applied prior to the selection of an easy or hard task by the participant. The SPN was defined as the mean voltage within 200 ms prior to the reward feedback  [55]. In the second temporal interval, epochs of 700 ms (100 ms prior to reward feedback to 600 ms following reward feedback) were extracted from the raw data and analyzed, with a baseline correction between 100 ms applied before the feedback to the onset of the feedback (100 ms to 0 ms).