dataesr/scientific-paragraphs-categorization huggingface dataset:
1 ). However, two of the three cores within the Atlantic sector indicating LGM SSI fall inside the PMIP4 +1σ contour line (Fig. 1g ). Five of the seven locations indicating the possible presence of SSI are located north of all three MMMs, but again on or inside the PMIP4 +1σ contour line (Fig. 1g ). We note that the eight locations from the Atlantic sector representing a presence or possible presence of SSI are bordered by cores suggesting ice-free conditions, possibly indicating a sea-ice tongue protruding from the Weddell Sea. The reader should bear in mind that with limited proxy data points indicating SSI and multiple models simulating limited SSI, it is not uncommon to record the same summer model data sea-ice agreement percentage across different model simulations. With only six proxy data points indicating SSI, the PMIP3 MMM, which displays the smallest SSI extent across MMMs, shows the highest model data sea-ice agreement (Ta-ble 3).
2 Sv) compared Clim. Past, 18, 845-862, 2022  https://doi. org/10. 5194/cp-18-845-2022  to the PMIP4 LOVECLIM experiment (26 Sv), the differences in sea-ice extent between these three experiments are much smaller than the intermodel differences between all PMIP3 and PMIP4 simulations. This indicates the limitations of performing model data comparisons with a single model to infer SO climatic conditions. We further assess the relationship between LGM SSI and AMOC strength (Fig. S2 ,  Muglia and Schmittner, 2015; Kageyama et al. , 2021) , and find that there is no statistically significant relationship between the two (R 2 = 0. 04). There is, however, a weak relationship between SSI extent and AMOC depth (Fig. S2, R 2  = 0. 17), with a shallower AMOC generally associated with a larger SSI extent.
Acknowledgements. This research is a result of the  Past Global Changes (PAGES) working group "Cycles of Sea-Ice Dynamics in the Earth system" (C-SIDE). We thank  Masa Kageyama  for providing the PMIP4 LGM outputs of the IPSL model. Ryan Green  was supported by a summer scholarship provided by the  Australian Research Council Centre of Excellence for Climate Extremes  ( CE170100023 ), and  UNSW  ( through Laurie Menviel's UNSW Scientia fellowship ). Computational resources were provided by the  NCI National Facility  at the  Australian National University , through awards under the  National Computational Merit Allocation Scheme , the  Intersect Allocation Scheme , and the  UNSW HPC at NCI Scheme. Financial support. This research has been supported by the  Australian Research Council  (grant nos.
Data availability. The ocean and sea-ice data for the LOVECLIM sensitivity runs can be found at https://doi. org/10. 26190/K6XA-T076 (Menviel et al. , 2022). The PMIP3, PMIP4 and LOVECLIM multi-model mean data can be found at https://doi. org/10. 26190/unsworks/1636 (Green et al. , 2022). PMIP3 data can be found at https://esgf-node. llnl. gov/search/ cmip5/ (last access: 15 March 2022) and most PMIP4 data can be found at https://esgf-data. dkrz. de/search/cmip6-dkrz/ (last access: 15 March 2022). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/cp-18-845-2022-supplement.
2018b , 2021b)  have made it possible to deepen our understanding of the known velocity and phase-space features, and also to discover many new ones. In the extended SNd the Gaia data have provided more E-mail: sergey. khoperskov@gmail. com evidence for the lack of equilibrium of the MW disk. For instance, previously known vertical wave-like motions  (Widrow et al. 2012)  were detected in zv z space in the form of a snaillike structure  (Antoja et al. 2018 ). The precise measurements from Gaia also provided a more comprehensive view on the inplane phase-space structure of the MW  (Gaia Collaboration et al. 2018b; Kawata et al. 2018; Antoja et al. 2018) , allowing a number of radially extended features and short kinematic arches to be identified in v R -v φ velocity space  (Ramos et al. 2018; Khanna et al.
Fig. 3  shows that in our high-resolution simulation single guiding space overdensities dominate individual spiral arms. However, this does not rule out the possibility that in other simulations, depending on the dynamical nature of the spiral arms, several such overdensities contribute substantially to a single arm (see, e. g,  Hunt et al. 2020). In this case, the XY-locus of the guiding space overdensities would overlap, which we have not observed in the available Gaia data  (Khoperskov et al. 2020b) , at least for the most prominent features. This issue could be further tested with forthcoming Gaia releases. Another approach for locating the spiral arms in this model is shown in frame (h) of Fig. 3  (see also frame (e)), where we see that the density maxima of the spiral arms also correspond to maxima in the fraction of stars near their guiding centers.
Specifically, a map of the fraction of stars with |R-R g | < 0. 25 is shown together with the same filled circles in panels (f) and (g) which represent the mean locations of the spiral arms in the (X, Y). We see a good correspondence of the spiral arms with a higher fraction of dynamically cold stars. This suggests that also in the Gaia data we can trace the mean locations of the MW spiral arms by finding the regions with a larger fraction of stars with low |R -R g | (see Section 3. 1). Note that a larger fraction of stars with cold orbits near the spiral arms does not necessarily suggest that these stars formed locally in the arms. Likely these dynamically cold disk populations react more efficiently, compared to kinematically hot stars, to gravitational perturbations of the spiral arms (see, e.
MW phase-space In this section, using Gaia data we explore the structure and kinematics of stars in the SNd v R -v φ space, (R,v φ ) coordinates, and in the Galactic plane, indicating how the most prominent phase-space features are linked to the MW stellar density structures (spiral arms and main bar resonances). We will use the dynamical structure found in our N-body simulation as a guide to find the correlations between in-plane overdensities in the guiding coordinates and the diagonal ridges and moving groups. Gaia (EDR3+DR2) is the largest available 6D phase-space dataset for ≈ 7. 2 million stars brighter than G RVS = 12 mag which is making possible precise studies of the MW structure on large scales and trace the kinematics of the disk across multiple kpcs around the Sun. For this work, we first took all sources with an available 5 parameter astrometric solution (sky positions, parallaxes, and proper motions) from Gaia EDR3  (Lindegren et al.
These overdensities overlap with the radial velocity pattern (right frames), first discovered in  Schönrich & Dehnen (2018)  and discussed also in  Friske & Schönrich (2019)  who interpreted the complex velocity pattern as the manifestation of the spiral structure. More recently  Wheeler et al. (2021)  suggested that some of the angular momentum overdensities can be associated with the main resonances of the MW bar (see, also,  Monari et al. 2019a). Since the G3RV2 dataset, while covering a large area in the MW, is still dominated by the stars in the extended Solar vicinity, it is important to test to what extent the density-velocity patterns we observe are caused by the local stars. In the bottom frames of Fig. 5  we show exactly the same maps as in the top but for stars located outside 1 kpc from the Sun.
Gaia and spectroscopic surveys: chemical abundances in the SNd, (R,v φ ) , (X g , Y g ) , and R g -z/V z A combination of stellar kinematic and abundance information based on high-resolution spectroscopy is a valuable instrument to explore the present-day properties and evolution of the MW stellar populations. To study chemical abundances trends associated with the MW spiral arms and bar resonances, we cross-matched the G3RV2 catalogue with Galah DR3, APOGEE DR16 (astroNN) and LAMOST DR5 stars. We adopted the data from the public version of GALAH DR3  (Buder et al. 2021)   2  where stellar abundances are derived using the Spectroscopy Made Easy code  (Piskunov & Valenti 2017). For our study we used only stars with SNR > 20, cannon_flag = 0 and abundance_flag < 3 which result in a precision of individual abundances of ∼ 0.
Therefore, we apply similar approaches to the Milky Way data. In our analysis of Gaia (DR2+EDR3 we find: -The angular momentum overdensities in DR3V2 data are seen independently within and outside 1 kpc from the Sun, just as in the homogenized Gaia data subset in our earlier study  (Khoperskov et al. 2020b). They are large-scale, not local features in the surveyed Galactic disk region (Fig. 5 ). -Most of the Gaia stars in the angular momentum overdensities are found close to their guiding radii (e. g. , ≈ 50% within |R -R g | < 0. 4 kpc, 95% within 2 kpc, see Fig. 6 ). For several angular momentum overdensities, the stars within 250 pc of their guiding radii are found in the Galactic plane in locations coinciding with the high-mass star formation regions from  Reid et al. (2019)  -i. e. the Sagittarius, Local, and Perseus spiral arms (see Fig.
Future Gaia data releases will have increased spatial coverage of the MW disk, making it possible to map the angular momentum structures at larger distances from the Sun and further test this interpretation. The physical parameters of the stars in the overdensities obtained by future spectroscopic surveys (4MOST, WEAVE, MOONS) will allow us to better constrain the mechanisms for the discovered metallicity enhancements and in this way shed light on the true nature of the spiral structure in the MW.
19 kpc (Gravity Collaboration et al. 2018), a velocity of the Local Standard of Rest, v LS R = 240 km s -1 (Reid et al. 2014), and a peculiar velocity of the Sun with respect to the LSR, U = 1 https://gitlab. com/icc-ub/public/gaiadr3zeropoint 11. 1 km s -1 , v = 12. 24 km s -1 , W = 7. 25 km s -1 (Schönrich et al. 2010).
Acknowledgements. The authors thank the anonymous referee for a constructive report. They also wish to thank  Jason Hunt  for stimulating this work. SK thanks  Dmitrii Byziaev ,  Jonny Clarke  and  Shola Wylie  for their assistance with access to the APOGEE data;  Evgeny Polyachenko  for the help with the initial condition generation for the N-body model;  Paola Di Matteo  for the access to data storage facilities of the  GEPI (Paris Observatory) ;  David Katz  and  Misha Haywood  for several suggestions about the Gaia and APOGEE data treatment respectively. SK also thanks  Ivan Minchev  for several helpful comments on earlier versions of the manuscript. SK acknowledges the  Russian Science Foundation (RSCF)  grant  19-72-20089  for support in the preparation of the N-body model partially carried by using the equipment of the shared research facilities of HPC computing resources at  Lomonosov Moscow State University  (project  RFMEFI62117X0011 ).
Based on Russel's work and considering the stated objectives, the integrative review will follow the steps shown in Figure  2. The steps are detailed below. (1) Identification of the topic. The choice of Web of Science (WoS), Scopus and Science Direct as search databases. "All years" was the reference period for the search, and the terms were "open_innovation*" and "supply chain," using underline and quotation marks to refine results. The search resulted in 178 papers: 30 in Scopus, 65 in WoS and 103 in Science Direct. (2) Inclusion and exclusion criteria. The first criterion was selecting the areas of management, business, economics and accounting, returning in 109 articles: 56 in Scopus, 42 in WoS and 11 in Science Direct. From this selection, only documents as "article" or "review" published in English were listed, which resulted in 73 articles: 29 in Scopus, 33 in WoS and 11 in Science Direct (maintained).
There were 17 duplicated papers, and two papers were not available, leading to their exclusion. Analyzing the scope of the remaining 54 articles led to the exclusion of another 26 papers. Thus, we selected 28 items for registration and subsequent analysis. We have developed this step based on peer review to reduce bias. (3) Registration of selected articles. After an in-depth reading and analysis, we plotted the selected papers in a synthesis matrix. The topics of synthesis matrix were as follows: search database, journal, author, year of publication, title, related country, keywords, research problem, objectives, central theme, sector of the chain studied, theoretical background, methodology, the chain's composition, the relationship among chain's components, knowledge (sources and means of exchange), reference to OI, the relationship between supply chain and OI, contribution to review, gaps for future studies, research limitations and relevant general observations identified.
A range of OTU similarity thresholds and ASVs were used to compare amoA (ammonia-oxidizing archaea [AOA] and ammonia-oxidizing bacteria [AOB]), nxrB, nirS, nirK, and nrfA communities between differing sedimentary structures. Significant effects of the sedimentary structure on weighted UniFrac (WUniFrac) distances were observed for AOA amoA when using ASVs, an OTU at a threshold of 97% sequence identity (OTU-97%), and OTU-85%; AOB amoA when using OTU-85%; and nirS when using ASV, OTU-90%, and OTU-85%. For AOB amoA, significant effects of the sedimentary structures on UniFrac distances were observed when using OTU-97% but not ASVs, and the inverse was found for nrfA. Interestingly, conclusions drawn for nirK and nxrB were consistent between amplicon reconstruction methods. We also show that when the sequences in the reference database are related to the environment in question, the BLCA algorithm leads to more phylogenetically relevant classifications.
However, when the reference database contains sequences more dissimilar to the ones retrieved, the NBC obtains more information. IMPORTANCE Several analysis pipelines are available to microbial ecologists to process amplicon sequencing data, yet to date, there is no consensus as to the most appropriate method, and it becomes more difficult for genes that encode a specific function (functional genes). Standardized approaches need to be adopted to increase the reliability and reproducibility of environmental amplicon-sequencing-based data sets. In this paper, we argue that the recently developed ASV approach offers a better opportunity to achieve such standardization than OTUs for functional genes. We also propose a comprehensive framework for quality filtering of the sequencing reads based on protein sequence verification.
In fact, some authors suggested that OTU similarity thresholds should be adjusted depending on the clustering algorithm and data complexity when phylogenetically divergent groups are present within the same community. This is because a single threshold for species delimitation is often not relevant due to the variable evolutionary rates of the 16S rRNA gene across lineages  (22, 23). Indeed, the use of strict thresholds has been shown to result in phylogenetically inconsistent (para-or polyphyletic) OTUs  (24, 25). After selection of the sequence similarity threshold, further choices need to be made. OTU clustering can be done using a closed-reference approach  (22, 26)  or by de novo assembly  (22, 26). With the latter method, reads are clustered into OTUs without comparison to a preexisting database.
This helps with the discovery of novel OTUs that are dissimilar to known sequences. However, the absence of comparison to known references makes de novo OTUs dataset-dependent, meaning that de novo OTUs from two different studies cannot be directly compared. On the other hand, the former approach makes it easier to compare OTUs between studies but limits the possibilities of discovering new sequences  (26). The open-reference OTU-picking method combines the closed-reference and de novo methods: reads are clustered against a reference database, and those that do not match reference sequences are clustered de novo. Even though this seems a good compromise between closed-reference and de novo methods, problems such as inflated richness and exaggerated between-sample diversity can still occur  (27).
With the ASV approach (  26 ), an error model is generated for the sequencing run, and reads are clustered in order to map this error model. The two most commonly used pipelines for ASV reconstruction are DADA2  (28) , in which both forward and reverse reads are denoised and merged, and Deblur  (29) , in which forward reads can be used on their own to reconstruct ASVs. This is advantageous when amplicons are too long to be merged properly or when the quality of the reverse reads is poor  (30). The ASV approach is appealing as it no longer groups amplicons based on a consensus sequence but instead resolves sequences with as little as a single nucleotide variation. Consequently, the ASV method does not require reference databases and is able to detect new sequences, and ASVs from different data sets can be directly compared  (26).
Consequently, ASVs are a promising approach for functional gene amplicon studies  (13). However, how the use of ASVs versus OTUs impacts ecological interpretations based on functional genes is unclear. After selecting the appropriate amplicon reconstruction method (ASVs or OTUs), the next immediate challenge is assigning taxonomy by matching against a reference database. For the 16Sr RNA gene, several curated databases are available (e. g. , Silva, GreenGenes, and Midas) and are routinely used. For most functional genes, no such databases are available, and the reference sequences used to assign taxonomy often vary among studies. The most popular approaches for assigning taxonomy rely on the pattern recognition of overlapping "words" of length k (generally k = 8), called k-mers. The frequency of matching k-mers between the query and reference sequences is used as a measure of sequence similarity: a higher frequency of shared k-mers indicates higher similarity between the query and reference sequences.
This approach is fast, objective, and not limited by the uncertainties associated with methods based on evolutionary models and alignments  (37, 38). This approach is usually implemented as a classifier, such as the commonly used naive Bayesian classifier (NBC). One main limitation of this approach is that it uses the assumption that the actual position of the k-mers in the sequence is not important, whereas in reality, two sequences with the same k-mers but in different orders are different. Furthermore, the optimal choice of length for the k-mer might vary depending on the target gene or the region within the same gene  (39). Another approach is the Bayesian lowest common ancestor (BLCA) algorithm  (39) , where the query sequence is subjected to a BLAST search against a reference database(s) and significant "hits" are recorded.
The taxonomy of the query sequence is assigned as the lowest common ancestor between these hits. For example, if a query sequence has significant matches to two Nitrosomonas europaea reference sequences, the query sequence will be assigned to the species Nitrosomonas europaea as it is the lowest common ancestor between the two hits. However, if a query sequence has two different hits, Nitrosomonas europaea and Nitrosomonas oligotropha, the query sequence will be assigned to the genus Nitrosomonas. By considering hit results from multiple databases, the BLCA approach is able to provide probabilistic-based confidence values at each taxonomic level of this assignment. Previously, the BLCA method was shown to provide better species-level resolution than the NBC for 16S rRNA gene sequences  (39, 40).
For nrfA, ammonia was also a strong driver (P , 0. 01) only when using ASVs, while nitrate and chlorophyll a were significant when using OTU-97%. NBC versus BLCA for taxonomic classification. For the majority of genes studied here, neither the NBC nor the BLCA method performed well, with the majority of ASVs being unassigned at the species level when using the Fungene database. This is likely an indication of the lack of environmental sequences with species-level-defined taxonomy in this database. The only exception was for nrfA, with the NBC approach resulting in only 17. 27% of ASVs being unassigned, versus the BLCA method, in which 100% were unassigned. Both NBC and BLCA performed better when a custom database was used to assign taxonomy (AOA and AOB amoA). In this case, BLCA performed slightly better than NBC for AOA amoA, with 72.
89% and 90. 85% of ASVs being unassigned with BLCA and NBC, respectively. Inversely, for AOB amoA, NBC performed slightly better than BLCA, with 0. 01% and 6. 68% of ASVs being unassigned with NBC and BLCA, respectively (Table  4 ). To determine what caused these differences for AOB amoA, a phylogenetic tree was constructed with AOB amoA ASVs and sequences from known AOB isolates downloaded from the NBCI database. The main differences in the taxonomic assignments for AOB amoA ASVs were for some sequences assigned as Nitrosomonas aestuarii with NBC that were unassigned using BLCA (Fig. 6 ).
This study also provides a comparison between NBC and BLCA for the taxonomic assignment of functional gene sequences. We found that the BLCA method performed better than the NBC for AOA amoA when using a custom database, and the inverse was found for AOB amoA due to some sequences being assigned to Nitrosomonas aestuarii when using NBC and being unassigned when using BLCA. When comparing these sequences to known representatives, it was observed that known AOB amoA sequences from Nitrosomonas aestuarii were clustered, and the common ancestor for all N. aestuarii strains did not include any of these ASVs. In fact, based on this tree, N. aestuarii and Nitrosomonas marina share an ancestor that does not include any ASV sequences. Based on this phylogeny, these ASVs could therefore not be members of the N.
aestuarii group (Fig. 6 ). We conclude that for AOB amoA, the BLCA method, despite resulting in a lower number of sequences identified to the species level, resulted in a classification that made more sense from a phylogenetic point of view. This result is coherent with previous research showing the superiority of BLCA versus NBC for the taxonomic assignment of 16S rRNA gene reads  (40). However, the BLCA method performed much worse than NBC for the taxonomic assignment of nrfA sequences, with the vast majority (98. 8%) being unassigned sequences, versus 18. 8% with NBC. This likely reflects limitations in the database used rather than a problem with the BLCA method itself. Indeed, the majority of nrfA sequences in the Fungene database are full-length sequences originating from cultured microorganisms, which likely differ from sequences retrieved from the environment.
To test this hypothesis, a phylogenetic tree was drawn using the top 200 nrfA ASVs found in this study and sequences from the reference database (covering 220 different genera). ASV sequences and representatives from the reference database generally formed separate clusters in the tree (Fig. S3 ). As a result, it can be expected that when ASVs are subjected to a BLAST search against the reference database, the significant matches do not share an ancestor at the genus level, and as a result, the BLCA algorithm cannot assign taxonomy at the genus or species level. In summary, the results from this study indicate that when the reference database is relevant to the sequences amplified (e. g. , reference AOB amoA sequences from marine sediments to assign AOB amoA ASVs from marine sediments), the BLCA method is the best approach.