dataesr/scientific-paragraphs-categorization huggingface dataset:
A total of 118 samples were selected and sequence was subsequently obtained from at least one region of the genome from 107 samples to which a unique "isolate ID" was then assigned. For seven of these (all wild C. gigas or Saccostrea commercialis from the Georges River in the 2013-2014 season) sequence was obtained only for Region 3 which had no variation (see below); these isolates were excluded from further analysis, leaving 100 isolates available for analysis, all being samples from farmed C. gigas (Supplementary data 1). Of the 100 samples of C. gigas, 38 were collected during periods of OsHV-1 infection in the Hawkesbury River estuary north of Sydney NSW (from Coba Bay n=2; Kimmerikong Bay n=5; Mooney Mooney n=15; Mullet Creek n=10; Porto Bay n=6), 60 were collected in the Georges River estuary south of Sydney NSW (from Limekiln Bar n=1; Neverfail Bay n=2; Site A n=13; Site B n=10; Site C n=25; Sylvania Waters n=1; The Shed n=8), while two were collected in Tasmania (from Pitt Water n=2) (Figure  1 ).
Extraction Each sample was derived from whole animal or dissected mantle and gill tissues of one or more oysters, depending on their size (Supplementary data 1). The DNA extraction was performed according to previous studies  (Evans et al. , 2014; Whittington et al. , 2019). Briefly, oysters were collected in the field and transported directly to the laboratory where they were frozen at -80°C until processed. A 400±100 mg sample was homogenised by bead beating using sterile stainless steel beads (Aussie Sapphires) and 1 mL distilled water and placed into a TissueLyser II machine (Qiagen) for 2 min at frequency 30, repeated once. All samples were clarified by centrifugation at 1340 g for 2 min in a microcentrifuge and supernatants were removed and stored in sterile tubes at -80°C. Nucleic acids were purified using a 5X The amplicons were visualised using 5µl of each amplification product on a 2% agarose gel, assessed against a molecular weight marker and visualised using RedSafe (iNtRON Biotechnology) on a GelDoc transilluminator (Biorad).
DNA data processing For each region targeted, nucleotide sequences were aligned with the reference genome NC_005881. 2 and the two microvariant genomes KY242788 and KY271630, using MEGA10  (Kumar et al. , 2018)  with the MUSCLE algorithm. The sequences were trimmed and the chromatograms were again specifically checked for all mutations on forward and reverse strands. Base positions described in results are mapped to the reference genome NC_005881. 2. Phylogenetic trees were constructed using MEGA 10 with the Neighbour Joining method and 1000 replications of boostraps and the method based on Maximum Composite Likelihood was applied. Two trees were obtained: the first was based on the concatenated sequence of Regions 1, 2, 3, 4 and 6 (Region 5 was not included due to too few sequences -see results); the second tree was based on Region 1.
The trees were unrooted because the ancestry of the reference genotype is uncertain. In addition, the use of another malacoherpesviridae, Haliotid herpesvirus, was impracticable because the ORFs had no sequence in common making alignment impossible. The unrooted tree allows the analysis of diversity but does not orient the genotypes temporally. Nucleotide diversity (Pi) for Region 1 (C region) including the microsatellite region was calculated using DnaSP v6: DNA Sequence Polymorphism Analysis of Large Datasets  (Rozas et al. , 2017)  and compared with the results of  Mineur et al. (2015) , after adding six sequences (JN800089, JN800075, JN800082, JN800083, JN800088 and JN800072) to be consistent with these authors. Briefly, the calculation was performed on the alignment; the indel polymorphism module with the "Multiallelic"gap option was used to consider all InDel events.
Description of the variants All nucleotide sequences are available on the NCBI database and accession numbers are listed in Table  3. Results and metadata for each of the 100 samples is provided in Supplementary data 1. Sequence alignments are illustrated in Supplementary data 2. For Region 1, located at ORF 4, nucleotide sequence was obtained from 87 of 100 samples. None of the sequences were identical to the OsHV-1 reference sequence but they were partially similar to the microvariants, with the presence of most of the mutations except the two substitutions of adenine instead of guanine in the downstream region (base 178,696 and 178,702). However, they all presented a novel substitution of an adenine instead of a guanine in a different position (base 178,705), also in the downstream region.
Several mutations were observed allowing the identification of five different nucleotide sequences: G1, G2, G4, G6 and G7 (Figure  1 , Supplementary data 2). Eight of the sequences that grouped in G4 (Supplementary data 2) had 15 deletions  (base 178,558 -178,572)  in the microsatellite zone instead of the 12 deletions seen in microvariants  (Burioli et al. , 2017; Segarra et al. , 2010) , as was observed in OsHV-1µVarΔ 15 reported in  Martenot et al. (2012). For Region 2, situated at ORF 43, nucleotide sequence was obtained from 77 of 100 samples. They were all different from the reference sequence and the microvariants. They were separated into two groups, classified by one deletion of a thymine in the downstream region (base 60,504) (G8) for 36 samples and two deletions of two adenine in the upstream region (base 60,059 -60,060) for 41 samples (G9) (Figure  2 , Supplementary data 2).
In the third region coding for ORF 35 to ORF 38, nucleotide sequences were obtained from 96 of 100 samples and all were identical to the two microvariant sequences. The features included the large deletion zone of 606bp (base 52,251 -52,856) common to microvariants and the substitution of a cytosine instead of a thymine (base 52,885). They were classified into G10 (Figure  3 , Supplementary data 2). Eighty-seven of 100 samples presented an amplification of the fourth region located on the non-coding site between ORF 49 and ORF 50. All the nucleotide sequences presented a common substitution of an adenine instead of a guanine in the upstream zone (base 72,489) and two deletions of two thymine next to the middle zone (base 75,592 -75,593). However there were many differences compared to the reference genotype and microvariants consisting of 19 mutations over 408 nucleotides which were not observed in the microvariant genotypes.
These mutations allowed the characterization of 14 groups (Figure  4 , Supplementary data 2), confirming the prior observations of high variability in this region  (Batista et al. , 2015). Four samples of a nucleotide sequence named G12 presented a thymine as well as a cytosine (Y) at the same position (base 72,536). The chromatograms of this and all the other regions of these four samples comprising G12 were unambiguous. All samples which contained the G12 variant were pools of 5 spat (Supplementary data 1). For Region 5 sitting on ORF 11, nucleotide sequence was obtained from 42 of 100 samples. None had the large deletion observed in the microvariants. Even though the nucleotide sequences were close to the reference sequence, a few mutations were present allowing their classification into four groups (G31, G32, G33, G34) (Figure  5 , Supplementary data 2).
Description of the predicted amino acid sequences Predicted amino acid sequences were aligned with the reference and microvariant sequences. All alignments are illustrated in Supplementary data 2. Regions 3 and 4 were not included in this analysis because Region 3 had a large deletion of ORF 36 and 37, inducing overlap of ORF 35 and 38 and creating uncertainty about whether a longer ORF could be translated, while Region 4 was a non-coding area. For Region 1, two amino acid sequences were identified for ORF 4 among the five DNA sequences described above in part 3. 1. 1. They were both different to the reference genotype and the microvariants. The first group was named <<A>> and contained G1, G2, G4 and G7; it had an asparagine (N) instead of an aspartic acid (D). The second group named <<B>> contained G6 and had in addition a leucine (L) instead of a serine (S) (Figure  7 , Supplementary data 2).
, 2015). In this region alone, 14 groups were identified among the 87 samples (one variant for every 6. 2 isolates). Similarly,  Batista et al. (2015)  identified one variant for every 6. 0 isolates examined in a study of 18 isolates from mostly diseased C. gigas in Portugal. C region, corresponding to Region 1 in the present study, is the most commonly analysed part of the OsHV-1 genome. With the typical primers C2/C6 used for amplification there is representation of part of a non-coding region and part of ORF 4  (Batista et al. , 2015). Nucleotide diversity in the C region was calculated formally and compared to data published in 2015  (Mineur et al. , 2015). There were more sequences in public databases, but the nucleotide diversity had not changed markedly except for Europe where it appeared to have decreased (from 15.
Acknowledgements Sample collection and sequencing was funded by  Fisheries Research and Development Corporation Australia  under projects  2011/053 ,  2012/032  and  2014/040  and  The University of Sydney and LABÉO France. Sanger sequencing was undertaken by  Australian Genome Research Facility and Monash Health Translation Precinct Medical Genomics Facility Australia. Bruce Allford ,  Len Drake ,  Bob Drake  and  Robert Hill  are thanked for their invaluable assistance with field work. Shellfish Culture Tasmania  kindly provided all batches of spat that were deployed and sampled in cited research trials. Vickie Patten  and  Ann-Michele Whittington  provided skilled laboratory assistance with oyster processing.
Evolution of SCDs with time Other data could be also interesting to evaluate the change of SCDs over time. Google Earth satellite images may be watched through time and the onset of SCDs has been already observed in Russia  (Larin et al. , 2015)  as well as in the USA  (Zgonnik et al. , 2015). In areas where agricultural activity is intense, it can be noted that the efforts of farmers to cultivate the SCDs areas failed. A few years later, the vegetation anomaly related to the SCDs comes back. Further, in the same way, the Landsat images may be used to characterize SCDs evolution. In some specific regions, the timing of the vegetation indexes acquisition above the SDCs could influence the interpretation. Indeed, both NDVI and SAVI depend on rainfalls or snow precipitations. If those precipitations are important, the indexes can change drastically.
On the other hand, this pumping power actually corresponds to a very small volume of soil around the stem. Given the average permeability of soil at this depth, only 2 cm around the tube is drained  (Moretti et al. , 2021a). These analyses therefore only study a very small part of the soil and if we do not pump, it is even worse. The quality of the H 2 analysis is also a matter of debate during soil gas measurements since, as already noted, the drilling that precludes the measure could artificially generate H 2 and create artifacts  (Lefeuvre et al. , 2022). However, the large amount of "zero values" collected during all the field trip campaigns suggests that such artifact effects during drilling might be limited, if present. Supplementary material provides an extensive dataset of measurements, the "zero values" obtained over various acquisition campaigns in Namibia, Australia, Bolivia, Ethiopia, and the Republic of Djibouti clearly show that the H 2 generation when drilling a few tens of centimeters in the soil is rare.
DEM Triaxial Simulations A numerical model was created to test the detection algorithm using the Discrete Element Method (DEM) software LIGGGHTS  [43]. Data from the simulation is post-processed with MATLAB to extract the data presented in the following sections. The simulation setup consists of a prismatic box measuring 0. 15 × 0. 35 × 0. 45 m 3 with a fixed base at the bottom and five pistons in all other directions, as illustrated in Figure  9. The contact model uses Hertz contact law with a Coulomb friction limit and a damping force calculated through a restitution coefficient. The normal and tangential spring stiffnesses (k n and k t ) are directly calculated by LIGGGHTS as a function of the interpenetration between two grains and the chosen material parameters reported in Table  1. Additionally, a third spring is used to insert artificial rotational friction into each particle, introducing a new spring stiffness (k r ) calculated as a function of the normal and tangential stiffnesses.
In the non-carrier strain a single point mutation resulting in a threonine to methionine amino acid substitution was found in gna, one of the K capsule biosynthesis genes involved in D-GalpNAcA synthesis (equivalent to WpbO in P. aeruginosa). The capsule biosynthesis gene cluster of A. baumannii Ab09 assembled from reads mapped onto the reference strain ACICU (Genbank CP031380), was shown to be closest to that of strain NIPH KL30 (Genbank MN166189). In the carrier resistant strain no mutation could be detected in the bacterial genome but we found large amounts of non-integrated viral DNA that could be assembled into a complete Aci01-1 103kb genome sequence. This suggested that resistance resulted from episomal maintenance of the Aci01-1 phage DNA in the bacterium, as previously observed with pseudo-lysogens of P.
2. The generic term "residual biomass" gathers all unused or underused organic waste, residues, co-products or by-products  (Javourez et al. , 2021). EU was selected not only due to the scope of this work, but also because this region is already recognized as highly committed towards circular bioeconomy deployment  (Fritsche et al. , 2021). The residual biomass potential estimation for France (that is, available volumes, composition, current uses, etc. ) were derived from previous works  (Javourez et al. , 2023 (Javourez et al. , , 2022)). In total, 132 individual streams were documented covering all the residual biomass subcategories (see section SI. 1). Corresponding background data is available in the data repository. Hereafter, "feedstocks", "inputs" and "streams" are seen interchangeable, the same applies for "biomass" and bioresources".
Hereafter, the combination (set-up; variant) is referred to as "pathway". Second, a harmonized modeling granularity was de ned to ensure compatibility and consistency across the LCI models. Residual biomass streams were characterized with 10 biochemical substances (including proteins, cellulose, lipids, etc. ) and 8 elements (carbon, nitrogen, phosphorus, etc. ), referred to as "fractions", as in  Javourez et al. , (2023 Javourez et al. , ( , 2022)). Mass balances for all fractions were tracked across the process work ows through stoichiometric equations or fraction-speci c transfer coe cient proxies, depending on the physic-chemical phenomenon and the data availability of the speci c unit operation. Moreover, the selected pathways were found documented at a su cient TRL (>5) to model system synergies such as heat integration  (Cucurachi et al.
This is because BSF growth performances were set through the protein conversion ratio (PCR), and the rest of the unit operations deduced by mass balance. This modeling choice aimed to capture the effects of using a speci c residual stream within a standardized insect rearing diet formulated to maximize conversion e ciencies and reduce production variability  (Ribeiro et al. , 2022). Proteins were considered as the limiting macronutrients for insects' development, which is equivalent to allocate the services provided by BSF pathways as a function of the protein content of input feedstocks. Common PCR values for tailored insects' diets usually range between 25-60% for BSF, with an average of 37% (background data and discussion in section SI. 4. 4). The latter is in line with measured values of  Parodi et al.
Acknowledgements This work was carried out within the framework of the research project Cambioscop (https://cambioscop. cnrs. fr), partly nanced by the  French National Research Agency ,  Programme Investissement d'Avenir  ( ANR-17-MGPA-0006 ) and  Region Occitanie  ( 18015981 ). U. Javourez  was additionally funded by the  Metaprogram GLOFOODS (INRAE-CIRAD)  and by the  French National Research Agency  under the grant  ANR-18-EURE-0021. The main investigation was developed as part of the PhD thesis of  U. Javourez defended at INSA Toulouse (France)  on the 12 th of April 2023, available upon request. A non-peer review version of the present work was uploaded on the preprint platform Research Square and is available at https://doi. org/10. 21203/rs. 3. rs-3671083/v2. Authors wish to thank  Dr.
The numerical simulations suggest that fracture length and spacing have an important impact on its performance. Thermal short-circuiting can be easily achieved if the fracture network is modelled based solely on satellite image data, and hydroshearing may be less effective if the DFN is constructed based solely on outcrop data. The simulations suggest that combining the different datasets provides the best compromise between heat extraction, water losses, hydraulic impedance and thermal drawdown. Despite the uncertainties, the fracture data used highlights the importance of multiscale fracture analysis for heatflow simulations of geothermal reservoirs.
Simulating the design and performance of an EGS involves the construction of a discrete fracture network model. The model is used to understand how the natural fracture system responds to stimulation treatments, and subsequently how the reservoir responds to the production and injection of fluids, and ultimately, how much thermal energy can be produced. Since the scale of observation has an impact on the geometrical attributes of fractures, a multiscale fracture network characterization should be undertaken to avoid misrepresentation and mischaracterization of these attributes (e. g. ,  Hardebol et al. 2015; Meixner et al. 2018; Espejel et al. 2020; Bossennec et al. 2021 Bossennec et al. , 2022;; Chabani et al. 2021a, b; Ceccato et al. 2022; Frey et al. 2022; Vega and Kovscek 2022). In fact, access to an exhaustive dataset, allowing mapping at different scales in both surface (e.
As aforementioned, ideally, fracture data for an EGS study site should be collected at all scales and incorporated into the DFN to create the most realistic depiction of the natural fracture network. However, this is not always feasible in remote northern communities. Many of these communities do not have boreholes, or cores recovered from wells, and in many cases the quality of the satellite images is poor for a good analysis. Moreover, not all communities are covered by LiDAR. Fortunately, Kuujjuaq, the study site has a relatively good dataset to study the scale effect on fracture properties. Kuujjuaq is an Indigenous community in Nunavik (northern Quebec) that relies entirely on diesel for its heat and power. The objective of this paper was to analyze fracture properties at the core, outcrop, and satellite image scale in the vicinity of Kuujjuaq.
Description of the datasets and data analysis Structural lineaments were analyzed by interpretation of Google Earth satellite images and fracture information (i. e. , azimuth, dip, length, spacing, frequency, aperture and connectivity) was collected in outcrops and well cores. A structural lineament was defined based on O'  Leary et al. 's (1976)  definition of the word "lineament": "A lineament is a mappable, simple or composite linear feature of a surface, whose parts are aligned in a rectilinear or slightly curvilinear relationship, and which differs distinctly from the patterns of adjacent features and presumably reflects a subsurface phenomenon. ".
Satellite imagery The satellite imagery for interpretation of the structural lineaments was retrieved from Google Earth (Fig. 3 ). The outcrop analyzed is located on the east side of the Koksoak River and it is made mostly of gneissic rocks of the Ungava Complex (see Fig. 1  for geographical location). This image was chosen since it was the one with the best quality for analysis. The images near the community have a lower quality for interpretation of the structures. The satellite image was interpreted in ArcGIS version 10. 3 using the Editor-straight element function to manually depict the structural lineaments. Then, trace azimuth and length were evaluated as explained in the ESRI technical support webpage-How to: Batch calculate line direction using the Field Calculator (ESRI 2021). The information gathered about trace orientation was then used in Grapher (Golden Software 2022) to produce the rose diagram.
Outcrops Six sampling areas were selected to carry out fracture analysis (A1 to A6; see Fig. 1  for geographical location). Sampling area 1 (A1) is located near the Coop store in Kuujjuaq (Fig. 4 ). In this area, a total of 6 outcrops were analyzed (Appendix 1). Sampling area 2 (A2) is located outside of the community in an area informally called Trois lacs (Fig. 4 ). In this area, 5 outcrops were investigated (Appendix 1). Sampling area 3 (A3) is located within the community, near a new treatment center and Koksoak River (Fig. 4 ). In this area, fracture data was collected in 8 outcrops (Appendix 1). Sampling area 4 (A4) is located near the Hydro-Quebec diesel power plant (Fig. 4 ). In this area, 3 outcrops were analyzed (Appendix 1). Sampling area 5 (A5) is located near the graveyard (Fig.
4 ). In this area, fracture data was sampled in 3 outcrops (Appendix 1). Sampling area 6 (A6) is located near the dock (Fig. 4 ). This is the only outcrop with a vertical surface. The outcrops in sampling areas A1 and A2 are made of paragneiss rocks from the False Suite and the outcrops in A3 and A4 are made of diorite rocks from the Kaslac Complex. The outcrops in sampling area A5 are near the contact between the paragneiss rocks of False Suite and tonalite rocks of Aveneau Suite and fractures from both rock types were investigated. Sampling area A6 corresponds to the contact between gabbro rocks from the Ralleau Suite and tonalite rocks from the Aveneau Suite, and fractures from both lithologies were investigated. The linear scanline sampling method together with a transit compass corrected for the magnetic declination (22° in Kuujjuaq) and an electronic caliper were used to collect fracture geometrical attributes (i.
e. , azimuth, dip, size or length and aperture) and the position of fractures along the scanline. A total of 1 to 4 scanlines per outcrop were used in an attempt to fully characterize the outcrop surface. The number of scanlines per outcrop was chosen depending on the outcrop quality and homogeneity/heterogeneity of fractures observed. Grapher (Golden Software 2022) was used to build the rose diagrams considering the azimuths of the fractures observed and Stereonet version 11. 3. 7 (Allmendinger 2020) to produce stereonet canvas. PDFs were applied to characterize fracture properties (Appendix 2). Then, statistical tests such as the RMSE and the SSE were used to evaluate which PDF best fit the data sampled (Appendix 3) as suggested by  Zeeb et al. (2013b).
Influence of scale effect The structural lineaments and fracture data collected from the satellite image, outcrops and well core were analyzed separately and combined to, first, understand how the scale of observation influences the geometrical attributes of fractures, second, evaluate which probability distribution function (PDF) best fits the data collected, and third, investigate the influence of scale effect on hydroshearing. A MvM distributions with 18 elements was applied to the outcrop data and suggests a unimodal distribution, with one single main fracture set oriented in the E-W direction (N81-90ºE; Fig. 7 ). Analysis of the satellite image, on the contrary, suggests a multimodal distribution with five main fracture sets (Fig. 7 ): F1-N81-90°E (E-W), F2-N41-50°E (NE-SW), F3-N101-110°E (NW-SE), F4-N21-30°E (NNE-SSW) and F5-N151-160°E (NNW-SSE).