dataesr/scientific-paragraphs-categorization huggingface dataset:
Origins. earth inventory Fossil fuel CO2 a priori fluxes used in this study are taken from a patent pending high-resolution inventory produced by Origins. earth (https://www. origins. earth/) over the IdF region, in combination with the global Open-source Data Inventory for Anthropogenic CO2 (ODIAC) product  (Oda et al. , 2019)  in surrounding areas. The Origins. earth bottom-up inventory is a gridded map of fossil fuel CO2 emissions within a rectangular (on a latitude and longitude grid) domain which encompasses most of the 5 IdF region (Figure  1 ) at 1 km × 1 km spatial and hourly temporal resolution. It provides the Scope 1 CO2 emissions from the year 2018 until the present time. For the simulation period from 2016 to 2017, we used CO2 emissions from the Origins. earth inventory for the year 2018 as the WRF-Chem model inputs.
Emissions from the tertiary, industry and energy have a relatively flat seasonal variation. In this study, the inverse emissions are compared to independent estimates from different inventories and a published study. These include (i) TNO-GHGco inventory at a resolution of 1/10º ×1/20º (lon×lat) (~6 km×6 km) for the years 2005-2020 (Denier van der 20  Gon et al. , 2021) , (ii) TNO-GHGco inventory at a resolution of 1/60º ×1/120º (lon×lat) (~1 km×1 km) for the years 2015, 2017 and 2018  (Dellaert et al. , 2019) , (iii) AirParif inventory developed by the local official air quality agency (https://www. airparif. asso. fr/en/) at a 1 km × 1 km resolution for the years  2005, 2010, 2015 and 2018, (iv)  the city-level CO2 emissions from the Carbon Monitor Cities dataset (https://cities. carbonmonitor.
Moreover, the wind speeds at downwind stations are imposed to be higher than 3 m/s to minimize possible contaminations from local sources of CO2 emissions near the measurement sites that cannot be reproduced by the model. However, the selection of a limited number of suitable data produces more uncertain estimates over periods when no observations are selected. The high sensitivity of the inverse emissions to the diurnal variations of prior emissions also highlights the limitations induced by assimilating data only during the afternoon. Therefore, we attempt to include morning CO2 concentration data in this study. Given that atmospheric models have difficulties in correctly reproducing the mixing processes under stable conditions and when the boundary layer develops in the morning, caution is required when assimilating morning CO2 concentrations.
Annual emission estimates Verification of annual emission totals and tracking emission trends over time using multiple methods constitutes an indication of the reliability of inverse emissions estimates. Moreover, given the potential impact of atmospheric transport errors on the emission estimates, and assuming that the model errors do not change statistically over time, the emission trends are expected to be less sensitive to model biases than the emissions estimates. The inter-annual variations are a critical metric to assess the effectiveness 25 of the CO2 mitigation efforts. Figure  5  shows the multiyear trend in the annual total fossil fuel CO2 emissions over the IdF region for the period 2005-2021. The prior and inverse emissions are compared to multiple inventory datasets. The interannual variation in fossil fuel CO2 emissions indicates a decreasing trend over the IdF region during the 2005-2021 period using the Mann-Kendall (MK) trend test at 5% level of significance (p-values (0.
Conclusions and discussions This study shows the capacity of our city-scale inversion system, with a state-of-the-art inventory and high-precision continuous CO2 measurements, for a timely and effective monitoring of urban fossil fuel CO2 emissions over a long-term time period from 2016 to 2021. Our results indicate a decreasing trend in the annual CO2 emissions over the IdF region with an amplitude of ~2% 10 per year at 5% level of significance. The comparison of both prior and posterior annual emissions with independent estimates from other inventory datasets shows a less than 10% difference, which a satisfying target in terms of emission trend detection and verification for Paris to support its emissions-mitigation measures and related policy  (Wu et al. , 2016). In practice, few cities have such a high-resolution near-real-time local inventory like Paris.
The uncertainties in the posterior estimates of CO2 emissions are caused to a certain extent by errors in the spatio-temporal distribution of emissions at scales finer than the targeted ones. The present inversion system mainly controls the city-scale fossil fuel CO2 emissions, but not the finer resolution in space. We thus further conducted a sensitivity inversion test using the TNO 1km inventory as a priori as an alternative to the Origins. earth dataset for 2018. The other parameters were kept identical to the reference 25 inversion configuration. The TNO inventory indicates more fossil fuel CO2 emissions concentrated within the Paris inner city compared to the Origins. earth dataset (Figure  S12 ). The inversion was able to spatially differentiate between the Paris city center and the Greater Paris region (excluding Paris), correcting the respective emissions differently (Figure  S13 ).
This could be achieved 10 by improving the default VPRM model with a modified gross primary production and ecosystem respiration equations, domainspecific optimized parameters, high-resolution input datasets and high-quality diagnostic phenology41. In addition, measurements of carbon isotopes ( 14 C, 13 C) and tracers co-emitted with CO2 (e. g. , CO, NOx, VOCs) could also be used to separate the contributions from fossil fuel and biogenic components to the total CO2 concentrations, which would be beneficial for the optimization of sectoral CO2 fluxes.
Acknowledgements The authors would like to thank  SUEZ Group  and  La Ville de Paris  for the support of this study. Thanks to  Marc Jamous  at  CDS , to the  IPSL QUALAIR platform  team and to  Cristelle Cailteau-Fischbach  ( LATMOS/IPSL ) at  JUS , and to LSCE/RAMCES technical staff for the maintenance of the CO2 monitoring network. Thanks also to  Pauline Buysse ,  Jé ré mie Depuydt ,  Daniel Berveiller  and  Nicolas Delpierre  for providing the eddy covariance flux measurements at  Grignon  and  Fontainebleau  ( Barbeau ).
Each metagenome was sequenced on one full lane of HiSeq 4000 with 150-bp paired-end reads yielding between 285 and 339 million reads per metagenome. Decontaminated, trimmed and normalized metagenomic sequences were co-assembled using MEGAHIT v1. 0. 4  [28]  with the default parameters and the -presets "meta-large" option resulting in 949,228 contigs of at least 1000 bp (Supplementary Tables  2  and 3 ). Prodigal was used for annotating open reading frames (ORFs) in the metagenomic mode (-meta)  [29]. The MetaW-RAP pipeline was implemented to recover individual genomes from the assembled contigs with three binning tools, including CONCOCT  [30] , MaxBin v2. 0  [31]  and MetaBAT v2. 0  [32] , and yielded 133 MAGs. The completeness and redundancy of each MAG were estimated by CheckM  [33]. Taxonomic classification of the 133 MAGs was determined by the classify_wf workflow of the GTDB-Tk toolkit  [34]  based on the Genome Taxonomy Database (GTDB v0.
Protein database construction and annotation of transporter proteins A total of 3,003,586 protein-coding genes were identified from the assembled metagenomic contigs by Prodigal as mentioned above. Protein sequences were clustered by CD-HIT-2D  [42, 43]  (-c 0. 9 -n 5 -d 0 -S 2) to eliminate redundancy. The resulting nonredundant proteins were pooled with the Global Ocean Sampling (GOS) amino acid sequence database in order to include more protein sequences from the marine environment, stemming from longer reads and thus more robust for the purpose of the analysis  [44]. Another round of cd-hit clustering (-c 1 -n 5 -d 0) was performed to remove identical amino acid sequences resulting in 58,403,522 sequences (Supplementary Fig. 2 ). In order to identify genes encoding transporter proteins in the individual metagenomic assemblies, predicted amino acid sequences were queried against the KEGG database with GhostKOALA  [42]  and eggNOG5.
0  [40, 41]  using eggnog-mapper v2  [45]  with default parameters. Sequences classified as "transporter" in the KEGG database were retrieved from metatranscriptomes and metaproteomes (provided as electronic supplemental material). Transporter families and compound specificities were verified by manually checking the KO number with assigned classification from the Transporter Classification Database  [46] [47] [48] [49]. For definite assignment of a protein sequence with a MAG, the sequences corresponding to transporters were aligned against the high-quality curated metagenomic bins, and those with 95% identity and 90% coverage were kept for MAG-specific further analysis. To focus specifically on carbon and Fe utilization, we used existing hidden Markov models for carbohydrateactive enzymes (CAZymes) (  [50] ; http:// www.
Transporters for organic carbon and iron A total of 144 and 156 transporter transcripts and proteins were identified by metatranscriptomics (MT) and metaproteomics (MP), respectively, of which 98 transporters were shared between the two datasets. The proportion of transporters made up between 3. 5 and 12. 3% of the total normalized transcript counts in the metatranscriptomes, whereas their proportion in the metaproteomes ranged from 42. 8 to 96. 8% (Supplementary Fig. 3 ). Membrane transporters belonging to the ATPbinding cassette transporter family (ABC) had the highest contribution (MT range 2. 8 to 10. 8%; MP range 21. 2 to 88. 9%), followed by outer-membrane receptors (OMR) (MT range 0. 22 to 0. 77%; MP range 3. 6 to 19. 6%) (Supplementary Fig. 3 ). A similar distribution pattern of these transporter types was observed previously in surface waters of East Antarctica  [55]  and other ocean regions and depth layers  [12, 56, 57].
Membrane transporter profiles were significantly different between seasons in both the MT and MP datasets (ANOSIM, p < 0. 001) (Fig. 1 ). We further explored these patterns for each of the MT and MP dataset. Transcripts encoding for protein families related to Fe transport showed significant differences in their relative abundances between both seasons (Fig. 2 ). However, most protein families (with the exception of PF01325 and PF02742 related to transcriptional regulation of Fe metabolism) contained transcripts that either increased or decreased in relative abundance. As a consequence, no seasonal trend in Fe-related transport proteins was observed in the MT data (Fig. 2 ). Similarly, the proportions of transporters for organic substrates were not different between spring and summer (Supplementary Fig.
There is a temporal decoupling that needs to be considered as proteins persist in a bacterial cell longer than the mRNA that encodes them  [69] [70] [71] [72]. In addition, while half-life times are on average higher in protein data, there might be continuous new production for both entities as well, thus the 'standing stock' might not be influenced by half-times only. Posttranscriptional as well as post-translational regulation are also processes that can explain why mRNA levels do not correlate with protein abundance  [73, 74] , however, we consider that these likely play a minor role. Methodological aspects further include a generally higher throughput and resolution of RNA sequencing compared to mass spectrometry-based shotgun proteomics. These technical aspects are reflected in our dataset.
Membrane transporters contributed the majority of proteins in metaproteomes but showed substantially lower relative contributions in metatranscriptomes (Supplementary Fig. 3 ). An overall comparison of the metatranscriptome and metaproteome datasets showed a qualitative agreement regarding expression differences of functionally annotated genes (Supplementary Figs. 5  and 6 ). However, relative abundances of transcripts differed greatly from those of their corresponding proteins, as has been found in earlier studies  [59, 69]. Taken together, the different patterns observed in the present study might reflect post-translational regulation involving protein modifications and proteolysis potentially leading to accumulation of specific proteins. Our data suggest that a large number of genes are differentially expressed at both seasons, but only a subset of these gene transcripts was recovered in the metaproteomes and can only in part be explained by the higher detection limit of the method.
Differential gene expression by active members of the community We then investigated the extent to which seasonal patterns in community-level expression of transporter proteins are detectable at the level of individual taxa. To link the expression of transporters of organic substrates and Fe to prokaryotic taxa, metagenome-assembled genomes (MAGs) were constructed, and gene sequences encoding for transporter proteins recovered from metatranscriptome and metaproteome datasets were mapped to metagenomic bins (completeness >= 50%, redundancy <= 10%; see supplementary material). A total of 133 high-quality MAGs spanning 11 phyla were obtained  [21]. Gene transcripts encoding transporter proteins could be mapped to most MAGs (122 out of 133), while proteomic peptides of transporter proteins could be mapped to 50 MAGs (Supplementary Fig.
7 ). This is likely a result of the higher RNA-sequencing depth used in our study providing a higher sensitivity compared to mass spectrometry-based analyses of peptides as described before. We retrieved all transporter proteins present in both datasets that could be mapped to MAGs and used the respective transcripts for differential expression analyses (Supplementary material). Significant differences in transcript abundances of transporters were observed for 25 MAGs when comparing spring and summer communities, and these seasonal changes were taxon specific (Fig. 4  and Supplementary Fig. 8 ). Within the same MAG, either transporters for C or Fe, but not both transporter types, showed differential abundance patterns, with the exception of one MAG belonging to Rhizobiales. The highest numbers of transcripts coding for transporter proteins, which were significantly different between seasons, were associated with Pseudomonadales and Nitrincolaceae.
In contrast, transcripts of amino acid transporters assigned to MAG 133 (Alphaproteobacteria HIMB59) had higher relative abundances in early spring. The role of these taxa in the expression of transporters for Fe (MAGs 103 and 62) and organic C substrates (MAG 115, MAG 133) in spring and summer was confirmed in the metaproteome dataset (Fig. 5 ). This view of the seasonal expression of membrane transporters illustrates the diverse ecological strategies for C and Fe acquisition among taxa and how they could shape expression profiles at the community level (Fig. 2 ). Our results point to potentially opportunistic clades, such as Pseudomonadales and Nitrincolaceae  [13, 22] , and groups with a more restricted repertoire of expressed transporters, such as Alphaproteobacteria and Flavobacteriaceae.
Acknowledgements We thank  S. Blain  and  B. Quéguiner , the PIs of the  SOCLIM and MOBYDICK  projects, for providing us the opportunity to participate to the respective cruises (https:// doi. org/ 10. 17600/ 16003 300 and https:// doi. org/ 10. 17600/ 18000 403), and the captains and crews of the  R/V Marion Dufresne  for their enthusiasm and support aboard. We thank 3 anonymous reviewers for their insightful comments that helped improve a previous version of the manuscript.
Funding This work was supported by the  French oceanographic fleet  (" Flotte océanographique française "), the  Climate Initiative of the BNP Paribas Foundation  ( SOCLIM -Southern Ocean and Climate ), the  French ANR  (" Agence Nationale de la Recherche ",  AAPG 2017 programme ,  MOBYDICK  Project number:  ANR-17-CE01-0013 ), the  French Research programme  of  INSU-CNRS-LEFE/CYBER  (" Les enveloppes fluides et l'environnement " -" Cycles biogéochimiques,environnement et ressources "), the  French Polar Institute  ( Institut Polaire   Paul Emile Victor ) as well as the  Austrian Science Fund (FWF)  Grant  P28781-B21  and  AI497821.
KERFIX Fe metabolism early spring vs. late summer FutA2 family iron binding proteins PF1325 Iron dependent repressor dtxR family N PF02742 Iron dependent repressor dtxR family FpvE domain PF01475 Iron dependent repressor Fur family FpvD family siderophore transport HatD family substrate binding protein Sid PvdS regulator Paeruginosa PA2426 180620 HumY substrate binding protein FeoB family iron transporter LbtU family siderophore receptor TonB family PirA family siderophore receptor PvdT family siderophore export FbpB family iron permease FutA1 family iron binding proteins YfeA family iron binding proteins YfeB family membrane proteins FbpA family iron binding proteins Log2 Fold Change of transcripts Log2 Fold Change of transcripts -Fe metabolism early spring vs. late summer FpvD family siderophore transport HatD family substrate binding protein Sid PvdS regulator Paeruginosa PA2426 180620 HumY substrate binding protein FeoB family iron transporter LbtU family siderophore receptor TonB family PirA family siderophore receptor PvdT family siderophore export FbpB family iron permease FutA1 family iron binding proteins YfeA family iron binding proteins YfeB family membrane proteins FbpA family iron binding proteins.
1 ; Methods). The number of events per km along strike was highest (2. 5 events/km) in segment B3 (Fig. 1b, c ). There were 0. 54 and 1. 36 events/km in B1 and B2, respectively, and 0. 98 and 0. 82 events/km in A1 and A2, respectively. Depths were su ciently constrained for 89 events (Methods). These events were distributed from just below sea oor (bsf) to ~ 22 km bsf, mostly at < 15 km depth (82 events). 7 events were located between 19 and 22 km bsf, well below the depth of the predicted brittleductile transition based on the 600°C isotherm from simple thermal models (Fig. 2 , Extended Data Fig. 3 ). The deepest events were located directly beneath the WFSs and the ELFS. For 47 events it was possible to determine robust focal mechanisms (Fig. 1a , Methods). Most of those earthquakes (34) occurred with predominantly strike-slip focal mechanisms.
Seismic Velocity and Thermal Structure We interpret our result considering two seismic velocity models. We present a P-wave refraction model to enhance our interpretation of shallow (< 20 km depth below sea level) depths beneath the ELFS (Fig. 2c , Methods, Extended Data Fig. 5 , Extended Data Fig. 6 ). We also compare our result to thermal structure predicted from the S-wave model derived from local Rayleigh and ambient noise tomography (Methods)  29. We preferentially interpret the P-wave model at shallow depths beneath the ELFS, but also considering the S-wave model for deeper velocities and coverage along the entire transform (Methods).
1-1. 2) with very low amounts of energy released seismically (4%). The presence of uids causes a series of mechanical and chemical effects, potentially increasing the pore pressure and reducing the effective normal stresses. This in turn can limit the maximum earthquake magnitude, potentially switching the rheological behaviour from brittle fracture of large earthquakes to aseismic creep and/or earthquakes with very small magnitudes  [32] [33] [34] [35]. Fluid migration itself can also result in swarms of very small earthquakes  13, 36, 37. The earthquakes may illuminate uid pathways, as uid penetration into the upper mantle can cool the lithosphere and extend the brittle ductile transition to deeper depths  21. Therefore, deeper observed seismicity in the OBS data beneath the ELFS may be explained if the cooling of the hydrothermal system has reduced the mantle temperatures depressing the depth of the brittle-ductile transition according to simple thermal models.
Seismic catalogue and focal mechanisms. The analysis of the broadband ocean bottom seismic deployment revealed 972 events, 812 of which are located within the area surrounded by the OBS network  31  , which belongs to the Chain TF and the adjacent ridge spreading centres (Fig. 1b ). The location of the events is performed by NonLinLoc software  42  using a 1-D velocity model of the Chain TF region based on CRUST1. 0  43  and earthquake arrival times  31. In order to focus on the TF seismicity, we discard the events that unequivocally occurred at the ridge segments and at the inside corners after considering their location and focal mechanisms (north-south striking, normal faulting events). This selection leaves 626 events along the Chain transform valley, within a local magnitude (M L ) range between 1.
1 and 5. 4. We further remove events below the completeness threshold M C =2. 3 (see below), resulting to a dataset of 370 events along the transform valley, which is used for our analysis. The epicentral coordinates of these events are su ciently constrained with median lateral uncertainties of 2. 5 km. Vertical uncertainties are larger (median ~18 km) and several events are above 20 km. For this reason, the hypocentre depths as well as focal mechanisms of well-recorded events are re-evaluated using the Grond software  44  , which carries out a Bayesian-bootstrap time-domain deviatoric moment tensor inversion. We calculated 119 focal mechanism solutions. For the stress inversion we select the 47 focal mechanisms classi ed as having good-ts (Fig. 1 ). We include 42 additional events (89 events total) with su ciently well-determined depth (mean vertical error of 6km).
1b ), which roughly corresponds to an average rate of ~1 event/day. However, the MLE returns considerably unstable b-values, even well above Mc, uctuating between 0. 76 and 0. 89 for 2. 3≤M L ≤3. 0 (Extended Data Table  1 , Extended Data Fig. 7 ). This is incompatible with the unique b-value predicted by the GR law and may lead to artifacts and misinterpretation of the derived outcome. For this reason, we apply alternative methods which are more suitable for small datasets and are less sensitive to M C selection  51, 52. The comparison of the results from the aforementioned methods, assuming different M C are shown in Extended Data Fig. 7. We choose to estimate b-values by applying the repeated medians  53  (RM), a non-parametric regression technique, which provides more robust results for small data sets, since it is highly resistant to observational uncertainties and outliers  51.
According to the RM approach, we consider magnitude intervals between two points, i and j, having N i and N j events, respectively, and The standard errors, SE, of the corresponding b RM are derived by bootstrap resampling process as described in Amorèse et al. 51. The signi cance of the b-value difference for two datasets, e. g. A and B, can be estimated by a bootstrap t-test, with the t statistic de ned as: A t=1. 96 corresponds to a signi cance at 0. 05 level. This approach returns b=0. 83±0. 09, with this value being insensitive to the applied magnitude cut-off (b=0. 82-0. 84 for 2. 0≤M≤3. 0) as shown in Extended Data Fig. 7  and Extended Data Table  1. We therefore apply the RM technique for all b-value calculations in this study, since it provides the most consistent results, regardless the M C choice.
The highest b-values are highest beneath the ELFS (1. 1-1. 2), they are lowest (<0. 8) in the central parts of the fault (~14. 7°E -13. 8°E) and intermediate (0. 9 -1. 1) in the western area (Extended Data Animation 1). Shallower water depths generally correspond to higher b-values in comparison to the deeper bathymetry areas, where b-values gradually decline with increasing depth at ~0. 15 units/km (Fig. 3a ). There are higher b-values (b=1. 04±0. 13) in regions of low rMBA (<-3mgal) than regions of high rMBA (<-3mgal) where b=0. 72±0. 11 (Fig. 3b, c ) with the difference between these two b-values being statistically signi cant at 0. 05 level, indicated by the calculated t statistic equal to 1. 96. Events also occur preferentially in areas with negative rMBA (Fig. 3d ). These areas are mainly located in the regions of shallowest bathymetry (<3700 m), i.