dataesr/scientific-paragraphs-categorization huggingface dataset:
[17]. While identical terms are often used in several of the 17 environmental packages currently available (https://gensc. org/mixs/), here we introduce three additional new terms: one is shared by several relevant MIxS environmental packages, and the two others will feature within the core MIxS package. The new term "observed host symbionts" provides a more comprehensive descriptor for the subject organism associations with smaller symbionts and it has been added to the host-associated, human-associated, plant-associated, human-vaginal, human-skin, humanoral and human-gut packages. The term "biotic relationship" has been added to the core package as a conditional descriptor of the relationship between the subject organism and other larger host organism(s). Finally, it appears necessary to include in the MIxS core a new term that takes into account the nested feature of most associations found in nature, such as host-symbiontmicroorganism, in which multiple packages are necessary to describe the samples of the study (e.
b, Angiosperm Orobanche hederae (S5, S6) parasitizing a host plant (P1, P2, P3) is reported using the included MIxS-SA and MIxS-PA (plant-associated) packages respectively. In addition, the MIxS-soil package is used to report corresponding soil samples. The following relationships are reported: S6 "within" P1, S5 "next to" S6, P1 "next to" P2, P1 "next to" P3, P2 "next to" P3, P1 "within" E3, P3 "within" E4, E3 "next to" E4. Observed host symbionts O a, Term may be slightly amended when included to all packages. b, Term added to host-associated, human-associated, plant-associated, human-v Req, Requirements: O, optional; C, Conditional; M, mandatory; TBD, to be de Table  1  terms, along with requirement status, description and MIXS IDs.
For example, if the symbiotic host organism being sampled is an intestinal worm, its local environmental context will be the term for intestine from UBERON (http://uberon. github. io/). Report the environmental material(s) immediately surrounding the symbiotic host organism at the time of sampling. This usually will be a tissue or substance type from the host, but may be another material if the symbiont is external to the host. We recommend using classes from the UBERON ontology, but subclasses of 'environmental material' (http://purl. obolibrary. org/obo/ENVO_00010483) may also be used. EnvO documentation about how to use the field: https://github. com/EnvironmentOntology/envo/wiki /Using-ENVO-with-MIxS. Terms from other OBO ontologies are permissible as long as they reference mass/volume nouns (e.
Acknowledgments This contribution is part of the  Parasite Microbiome  Project. The authors sincerely thank the  Gordon and Betty Moore Foundation  for fully sponsoring the 1 st  Parasite Microbiome  project workshop, and for supporting Open Access publishing. FJ was funded by a grant from the  Marsden Fund (Royal Society of New Zealand ) to RP (Principal Investigator) and NMD (Associate Investigator). The  National Institutes of Health  grants  RO1CA164719  (PJB), and  R01AI144016-01 (LJK). VH was supported by the  FEDER grant InFoBioS n°EX011185  (région Val de Loire). JMM acknowledges support from award  OCE-1933285 ,  National Science Foundation, USA. ML is part of the PARADISE consortium, supported by funding from the  European Union "s  Horizon 2020 Research and Innovation programme  under grant agreement No 773830:  One Health European Joint Programme.
A R Y In some Earth materials, significant induced polarization (IP) phenomena are occurring when an electric perturbation is applied. These mechanisms are described by a frequency-dependent complex resistivity (CR). The study of the CR spectral signature allows to access indirectly to several properties of interest of the subsurface linked to the interaction between the pore space and fluids. CR is usually studied using the electrical method with a direct current approximation, neglecting by the way electromagnetic (EM) induction that can occur in the data. However, EM induction increases with frequency and offset, resulting in limitations at high frequencies or for the investigation of deep target. We implemented a frequency-dependent CR in a 3-D finite-differences (FD) modelling and inversion code for frequency domain controlled-source electromagnetic (CSEM) data to take into consideration IP information contained in EM data or reciprocally.
The CSEM methods are resistivity imaging techniques using multifrequency EM fields that fully take into account EM induction with large investigation depth. Following a preliminary sensitivity study, a multistage inversion framework was designed to constrain the multiparameter inverse problem. Furthermore, to manage the increasing number of parameters, a second-order polynomial parametrization is used to describe independently frequency variation of CR norm and phase. We demonstrate the method through 1-D and 3-D synthetic data inversions for a deep-target model. We show that we were able to recover the CR and its frequency variation from CSEM data in the IP/EM coupling domain for 1-D targets. The problem of deep polarizable 3-D targets is more challenging and the resolution of the recovered CR spectrum was impacted.
EM induction increases with frequency, offset and depends on the wire-layout. Several empirical methods dealing with EM induction in DCIP surveys exist, as using EM removal techniques summarized in  Routh & Oldenburg (2001). Nevertheless, in many DCIP field-scale studies, the frequency range is limited to be able to neglect EM induction using only unaffected data  (Karaoulis et al. 2011; Orozco et al. 2012). A rigorous solution to EM/IP coupling is to take into account the full-EM response of the ground by solving Maxwell equations and considering the cable layouts. Controlled Source Electromagnetic methods (CSEM) use the full-field EM responses of the underground excited by a galvanic or an inductive source to recover the resistivity of the ground. CSEM data can be processed equivalently in the time domain or frequency domain  (Mörbe et al.
With recent improvements in instrumentation capabilities as well as the developments of high-performance computational facilities, the technical abilities to manage such complexity are spreading. Nevertheless, the need for CSEM tools able to handle such frequency-dependent CR without relying on a specific parametrization is still a necessity. To recover IP parameters from CSEM inversion new challenges arise with the multiplication of parameters. At least, one additional parameter per cell is necessary to introduce IP for the simplest constant CR case. This case is similar to the simultaneous inversion of dielectric permittivity and conductivity for high-frequency data (f > 100 kHz) as imaginary resistivity can be expressed as a permittivity. Some authors as  Lavoué et al. (2014)  and  Busch et al.
Virieux & Operto (2009)  highlighted in their paper on FWI, the importance of the parametrization choice for multiparameter inversion considering targets and acquisition configurations. They presented how some parameters can be coupled or not, as some parameters can explain the same part of the data, whereas some are complementary. We propose in the first part of this paper to present how IP is contaminating CSEM data set in the quasi-static frequency domain for a galvanic source. To better understand IP contamination in CSEM data sets, we present a 1-D sensitivity study of CSEM data to CR. At first, we discuss the simplest case of the electric field response of a homogeneous half-space with a constant CR and a CC based resistivity model to illustrate clearly the IP impact expected on a CSEM data set for a frequency-dependent CR case.
S E N S I T I V I T Y S T U DY At first, we evaluate the sensitivity of CSEM data to CR in order to better understand the IP signature in the EM data and define an efficient way to derive them. We restrain our study here to the case of the electric field response of the underground induced by an inline horizontal electric dipole (HED // ) (galvanic transmitter) located at the surface. This is the classic case of ERT and SIP method in a Dipole-Dipole configuration, except that electromagnetic induction is fully taken into account. SIP method assumes to work in the static case, whereas CSEM methods use electromagnetic induction to investigate spatially the underground with several frequencies by varying the skin depth δ ≈ 503 ρ f. The static case is respected when the offset r from the source is smaller than a wavelength (λ w = 2πδ) r λ w , corresponding to the 'near field', which is in opposition with the 'far field' where r λ w corresponding to the plane wave domain used in the magnetotelluric method.
Constant resistivity homogeneous half-space 1-D responses are computed using a 1-D semi-analytical (SA) code based on em1d code  (Kim et al. 1997). Fig. 1  presents electric field responses using normalized amplitude and phase spectra (left), or normalized in-phase and quadrature-phase representation (right), as a function of r/δ, with r the source-receiver offset and δ the skin depth. Responses are normalized by their total amplitude in the static case (i. e. using the value for the smallest ratio computed r/δ < 10 -1 ). This kind of representation allows visualizing the response from the near field (DC hypothesis) (r/δ 1) to the far-field (r/δ 1), independently to the resistivity amplitude of the half-space or the dipole moment. Electric field on a homogeneous half-space with respect to r/δ shows an equivalent effect of the offset or the frequency variation, as skin depth is proportional to the inverse of the frequency square root.
Contrary to electric field amplitude, IP effects on the electric field phase can easily be differentiated. Indeed, we observe that IP effects create a strong phase delay compared to the real and constant resistivity case. Phase effects around 5. 7 • (5. 7 • = 100 mrad) are observed on the complete spectrum, with a maximum around 8. 5 • in the transition zone for HED //. A minimum is observed around 3 r δ ≈ wavelength 2 due to a sign reversal of the quadrature electric field. Note that the electric field phases are shown in degree rather than using milliradian, as EM data are prone to large phase variations compared to SIP data. Furthermore, it allows to better differentiate the data (electric field phase) to the medium parameter (CR phase) and avoid confusion as they cannot be directly related in CSEM data (impacted by EM induction) contrary to SIP data.
( 7 ) It highlights that when looking at the amplitudes and phases of the electric field responses, evaluating the IP effect by comparing the model at |ρ * | constant should be preferred instead of keeping ρ r constant and the opposite when looking at real/imaginary parts of the electric field. Reformulated in a more practical way, it highlights that varying the CR phase by keeping the norm constant perturbs less the electric amplitude and reciprocally for the real/imaginary part. In this section, we have shown that a considerable IP effect caused only by the imaginary part of CR can be observed in CSEM data. IP effects magnitude is controlled by the CR phase φ cpx. The presence of an imaginary resistivity ρ q impacts mainly the phase of the data and has a weak effect on the amplitude of the electric field response, which shows significant IP effects only within a limited bandwidth in the transition zone due to supplementary coupling between EM and IP effects.
Buried layer Sensitivity to IP effects will depend on several factors in the case of a buried target. They are linked to the fundamental difference between geometrical and frequency sounding. DC method uses geometrical sounding to control investigation depth by varying TX-RX offset, whereas CSEM methods use frequency sounding and more often a complementary mix of both to control investigation depth. Indeed, the depth of investigation depends on the skin depth in the medium, decreasing with increasing conductivity and frequency. Sensitivity to the buried body depends on the offset and the frequency used. EM data will lose their sensitivity to the target at too short offset, depending on the survey geometry, or at skin depth shorter than the target depth. Fig. 7  shows the electric field of a 100 m thick conductive layer, considering a real or a complex constant resistivity, embedded at 100 m depth in a homogeneous half-space at 100.
I N V E R S I O N O F C O M P L E X R E S I S T I V I T Y In this part, we describe the inversion framework proposed to infer a 3-D CR model from EM data. Our work relies on the implementation of CR modelling and inversion into the code POLYEM3D  (Bretaudeau et al. 2021). This code is a 3-D finite-difference parallel code for modelling and inversion of CSEM and MT data in the frequency domain. We first introduce briefly the essentials of the original code and the straightforward implementation of CR to the forward problem. For the sake of clarity, and to assess and validate our inversion framework with reasonable computational times, we investigated the multiparameter inversion behaviour through a 1-D layered model inversion. We described through 1-D synthetic examples how we established an appropriate multiparameter parametrization and inversion strategy of the frequency-dependent CR.
Method The code POLYEM3D solves electrical formulation of Maxwell equation with a scattered field approach (eq. 9, Newman & Alumbaugh 1995). The primary field E p defined as the electric field in a 1-D reference medium σ p , is solved using the accurate and fast semi-analytical code mentioned in Section 2. 1 (em1d,  Kim et al. 1997)  whereas the scattered or secondary field E s is solved on an irregular Cartesian grid by a FD scheme (eq. 9) on a staggered grid formulation  (Streich 2009). ∇ × ∇ × E s + iωμ 0 σ E s = -iωμ 0 (σ -σ p )E p. ( 9 ) The imaginary number is noted i, ω represents the angular frequency and μ 0 the magnetic permeability in the vacuum. Separation of the primary and secondary fields allows to avoid singularity of the sources and manage numerical errors due to the fast decrease of the primary field from the source.
Only a small portion of the residual contains information on the shape of the CR model. We choose to use a 2nd order polynomial support to describe the CR spectrum adapted to a multistaged strategy. Indeed, we obtain thus two independent polynomials to describe the medium, each having different sensitivity to electrical data. As CSEM data are more sensitive to CR norm variation than to the CR phase, the use of a CC model allows to retrieve a CR phase spectrum even if data are not sensitive to it, which could be seen as an advantage but also misleading in cases with low information on the medium and low sensitivity. Looking for two independent polynomials allows to cross-check results between CR norm and phase and to pay more attention to what is reliable in the data. In most cases, 2nd order polynomials are expected to be sufficient to describe the CR model with an acceptable accuracy according to resolution limitation with depth and expected noise at the field scale.
Geometrical versus frequency sounding The previous cases were based on synthetic data sets with an ideal acquisition geometry and investigation frequency window considering the targeted chargeable layer dimension and depth. In CSEM surveys, a part of geometrical information needed in the DC method can be obtained using a frequency sounding, by scanning the medium with the variation of the skin depth and lowering slightly thus the number of TX/RX needed to obtain a correct image. Fig. 14  presents the result of stage (1) for two cases with different geometrical coverages and shows the importance of conserving the maximum of geometrical information to obtain a reliable frequency-dependent CR model. To ensure a perfect frequency sounding even for the shallowest layers of our inverted model, we use in these examples 15 frequencies logarithmically spaced from 10 -2 to 10 5 Hz, giving a minimal skin depth of 15 m for 100.
Stage zero Synthetic CSEM data were inverted with a GN optimization using LSQR method  (Paige & Saunders 1982; Grayver et al. 2013 ) to compute the model perturbation m at each iteration. Maximum smoothness regularization was used with a regularization parameter that can be estimated using an L curve. The inversions were undertaken using a remote supercomputing facility part of the French National Computing Center for Higher Education (CINES) equipments. Each inversion used 260 cores distributed over 10 nodes of 64 Go and required several tens of hours of computing. No performance test was carried out at the time of these computations as it was not the main goal of this work. However, to provide the reader an estimation of the computational requirements, we needed for the following 3-D examples roughly 40 s to analyse, factorize and solve the FD system of linear equations (eq.
9) and 200 s to compute the Jacobian and the gradient per frequency. Considering that 3-D problems are more challenging than the 1-D case and that IP effects are small, we started the inversion with a stage 'zero' prior to stage (1) defined previously to help the local optimization. The starting model is a constant resistivity medium of 100. m with a negligible CR phase of -1 mrad. In order to get closer to the true medium resistivity before inverting for frequency dependence, stage (0) inverts data to retrieve p 0. p 0 represents a constant resistivity norm medium similarly to a CSEM survey considering a constant and real resistivity. Note that the exponential of the zeroth order polynomial coefficients are presented in the following images in order to correspond to a CR norm [. m] or phase [mrad] as the polynomials are based on the logarithm of these parameters.
Fig. 17  presents the results of stage (0) for the two-cubes and the cube 1 models. No stage (0) inversion was performed for the cube 2 model as there is a low contrast between the background and cube 2, starting model data and synthetic observed data are already close enough. An additional image presents the inversion result of the IP contaminated data of the two-cubes model considering a purely real and constant resistivity inversion and is referred as a 'classical' CSEM inversion. For this case, no special care on IP contamination was taken to provide a comparison. Inversion iterations were stopped when acceptable rms were reached with a convergence rate considered too low. In a real case survey, a user with an experienced eye is needed to understand EM and IP contribution in CSEM data in order to manage these stages by detecting the low IP effect in EM data and having a bigger picture of the capacity to fit the data set according to the situation.
The starting rms for the cube 1 model is 20 per cent, whereas the rms of the cube 2 model, with low contrast anomaly is 2 per cent. The two-cubes model starting rms is about 22 per cent. Each image is close to the true model considering the diffusive limit of CSEM imaging. Cube 1 resistivity is slightly underestimated but well located, with a cube around 10. m in its center. Cube 2 cannot be separated clearly from the background at this stage in Figs  17(a ) and (c). Classic CSEM inversion is not severely perturbed by IP effects, nevertheless, data are not perfectly fitted with a remaining rms of 1. 33 per cent on amplitude, 0. 75 per cent on phase data and a total rms of 1. 53 per cent. The remaining total rms is already acceptable even without considering IP effects of the target at this depth, which highlights again the necessity of a very low noise data set in order to look for CR spectrum.
It allowed to retrieve a good CR spectrum shape for the norm and the phase of a buried layer. Nevertheless, even for simple cases using a 1-D model, IP sensitivity issues are already challenging when considering a buried target. Considering 3-D synthetic examples, the inverse problem is much larger and the IP sensitivity of CSEM data to buried and limited polarizable body is decreasing. Some encouraging results can nevertheless be highlighted here. Cube 2 CR spectrum trend was relatively well retrieved in the cube 2 and two-cubes synthetic models on the norm and the phase. Cube 1 failed to retrieve the correct CR norm spectrum due to equivalence problems. Nevertheless, the constant parameter and the main trend of CR phase spectral variations were correctly retrieved and well located during the second stage for the q 1 parameter.
Finally, the spectral behaviour of both cubes could also clearly be differentiated between themselves using the opposed slope q 1 observed on the CR phase. The resulting slopes on the CR phase let us differentiate between the long and the short relaxation time anomaly. We saw that CSEM data sensitivity to IP effects of deep 3-D targets is relatively weak even considering a large frequency-dependent CR anomaly (high chargeability, CR phase peak below -100 mrad). The IP signal gets close to numerical accuracy as the depth of the target increases and its size decreases. In our examples, we did not consider an additional noise. The presence of noise would certainly affect our capacity to retrieve correctly a CR medium and its frequency dependence depending on the measurement quality and the case-dependent cultural noise.
Therefore, data with an exceptional signal-to-noise ratio are necessary to expect to retrieve such results in the case of a real survey. However, it is possible to improve the signal-to-noise ratio by using proper data processing techniques as robust processing algorithms  (Egbert & Booker 1986; Streich et al. 2013). Furthermore, signal-to-noise ratio can be improved by increasing the source moment. It can be achieved by injecting a larger current by using a strong transmitter able to inject several dozen of amperes  (Streich et al. 2013; Mörbe et al. 2020)  and/or by increasing injection or receiver dipole sizes as cable-layout responses can be taken into account in the computation of EM fields. Great care should be taken for the positioning of the survey arrays. The positions of electrodes and wire layouts should be measured using a differential GPS positioning system able to reach a centimetric accuracy.
A C K N O W L E D G M E N T S The research and developments leading to these results have received fundings from the French National Research Agency (ANR) in the framework of the project EXCITING (ANR-17-CE06-0012). Large scale 3-D EM modelling and inversion were performed with BRGM's POLYEM3D code using the Occigen supercomputer thanks to GENCI (Grand Equipement de Calcul Intensif) and CINES (Centre Informatique National de l'enseignement Supérieur) supercomputing facilities. We are grateful to the reviewers, M. Bücker, W. Agudelo and an additional anonymous reviewer, for their valuable comments which allow us to improve this manuscript.
Ensuring code formatting conventions is an essential aspect of modern software quality assurance, because it helps in code readability. In this paper, we present STYLER, a tool dedicated to fix formatting errors raised by Checkstyle, a highly configurable format checker for Java. To fix formatting errors in a given project, STYLER 1) learns fixes for self-generated errors according to the project-specific Checkstyle ruleset, based on token sequence fed into a LSTM neural network, and then 2) predicts fixes. In an empirical evaluation, we find that STYLER repairs 38% of 11,220 real Checkstyle errors mined from 70 GitHub projects. Moreover, we compare STYLER with the IntelliJ plugin CHECKSTYLE-IDEA and the machine learning-based code formatters NATURALIZE and CODEBUFF. We find that STYLER fixes errors from a more diverse set of Checkstyle rules (24 rules, compared to CHECKSTYLE-IDEA: 19; NATURALIZE: 20; CODEBUFF: 17), and it uniquely repairs errors for two rules.