dataesr/scientific-paragraphs-categorization huggingface dataset:
GPT models can generate human-like text based on a given prompt or context. The latest version, GPT-3. 5 (https://chat. openai. com/chat) can work with 175 billion parameters, showing abilities in tasks such as language translation, question answering, and creative writing. Although, recently a larger model, GPT-4 has been released by OpenAI  (Heaven, 2023) , including its use in Microsoft utilities, such as Bing and O ce 365. Tools such as the previously mentioned and others have the potential to facilitate students' interaction and visualization, thereby enhancing their learning experience in product-based learning environments.
Literature review The study adopted the checklist and guidelines of Preferred Reporting Items for Systematic Reviews and metaanalyses (PRISMA) to ensure transparency in the search and analysis process  (Moher et al. , 2009). The PRISMA procedure involved a search of two digital libraries, removal of results based on exclusion criteria: removal of duplicates, initial screening of paper abstracts, collation of relevant full articles, and deeper synthesis of each article to extract relevant content to ensure consistency and interrelated reliability. Each manuscript was coded for information such as the paper title, authors, year, context, aspects of inclusiveness, methodology and the purpose of biobased materials research. A systematic search was performed in the Dimensions and Scopus databases using the keywords "biobased" + "engineering" + "materials" in the title and abstracts of publications.
The search was limited to 275 peer-reviewed journal articles written in English and published between 2011 and 2023. The bibliographic data obtained from the search was analyzed using VoSviewer text analytics module, which created a visual representation of bibliographic links. The weight of circles and lines in the visualization represent the number of publications, citations, and collaborations and the signi cance of the research output in various regions of the world (Figure  2 ). In Figure  1  are depicted the major innovation centers in countries where the circular bioeconomy has been integrated into engineering education. Four research clusters can be identi ed, with the leading countries being United States, China, Canada and India. The main collaboration is between the European Union and China (red cluster), being also important the collaboration between Canada and the so-called "boreal alliance" countries (Finland and Sweden, blue cluster), while the green cluster corresponds mainly to Asiatic countries.
Discussion In this work we have presented the implementation of GPT text generation AI in a Materials in Chemical Engineering course within a biobased materials framework. We demonstrated how prompts could be used to generate academic text with in-depth knowledge and how they can be used to explore speci c biobased materials, such as nanocellulose. The generated text can be used by students as a tool in the different stages of product development, including brainstorming, selection of the product, and standard operating procedures for production. However, despite the AI's capability to generate references, they may not always be accurate. Thus, it is still necessary for students to review and add appropriate references within the given context to ensure the generated text's accuracy. The approach discussed in this work offers a fast and e cient strategy for generation of ideas, brainstorming, and exploration of biomaterials in a scienti c language and in a product-based learning environment.
Acknowledgments: We want to acknowledge the professors, students, and research teams at  North Carolina State University  for valuable discussions on this subject during our time at this institution. Dr. Richard Marquez  from  Universidad Tecnologica de la Mixteca  is thanked for enriching discussions in AI and reviewing the Section 2. 2. Declaration of competing interest: The authors declare that they have no known competing nancial interests or personal relationships that could have appeared to in uence the work reported in this paper. Data availability: Data will be made available on request. CRediT Statement:  Ronald Marquez  Conceptualization, Methodology, Writing original draft, review & editing. Nelson Barrios ,  Ramon Vera ,  Maria E. Mendez ,  Laura Tolosa  Investigation, Writing, review & editing.
The paper introduces a software called Bulldozer, designed to extract a Digital Terrain Model (DTM) from a Digital Surface Model (DSM) obtained from various sensors. The software is based on a modified version of the multi-scale Drap Cloth principle to process noisy DSMs of any size, employing a tiling strategy and a stability margin to ensure consistent results. A parameter called max object size is introduced to differentiate objects from the ground during the drap cloth process. Gravity steps and ground distance sampling resolution are adjusted based on the input DSM. No-data and noisy values present in the DSM are detected and converted into no-data values to improve the quality of the Cloth simulation. The paper describes a memory-aware parallel execution strategy using both the multiprocessing and the shared memory Python modules.
As a reminder, the differences between a DSM, a DTM and a DHM are illustrated in Figure  1. This paper presents a scalable software, called Bulldozer, to extract a Digital Terrain Model (DTM) from a Digital Surface Model (DSM) provided by any type of sensor (LiDAR, Photogrammetric with stereo correlation). The proposed algorithm is a modified version of the multi-scale Drap Cloth principle  Leotta et al. (2019)  adapted to process noisy DSM of arbitrary size thanks to a tiling strategy and a given stability margin that ensures identical results to those obtained without tiling. We introduce an intuitive input parameter, called max object size, that defines what are the objects that have to be distinguished from the ground in order to prevent the drap cloth from falling into them. The values of the gravity steps are also changed and correlated to the ground distance sampling resolution of the input DSM.
No-data and noisy values are often present in the input DSM and this is particularly true for stereo ones. These outliers penalize significantly the quality of the Cloth simulation because they can be considered as aberrant ground points with a severe impact on the elevation of the neighborhood. To minimize the impact of such values to the quality of the resulting DTM, we propose an algorithm to detect noisy areas and turn them into no-data values. Then several strategies depending on the size of the no-data areas are applied to fill them with interpolated height information. On the computational aspect, we describe how the whole pipeline is executed in parallel thanks to the multiprocessing Python module and how the shared memory concept is used in order to reduce the memory footprint. To analyze and compare our results with DTM ground truth references, we have created a benchmark dataset containing both stereo DSMs computed with softwares such as CARSMichel et al.
Thoses results demonstrate the ability of Bulldozer to provide DTMs of high quality for various landscapes. • In Section 4, we discuss about computational and architectural aspects of Bulldozer. Bulldozer is open source and accessible on github and Pypi. It provides richfull interfaces via API and CLI. Bulldozer is also memory efficient and proposes a multi-processing execution of the DTM extraction. Finally, Section 5 describes the current studies on Bulldozer with the motivation to allows the software to be incremental (what we call external scalability), i. e, capable of extracting DTMs on parts of landscape without knowing the neighborhood and hydro compatible.
Results In The statistics are available in Table  1  and a profile showing the difference between the input DSM, the Bulldozer DTM and the RGE alti DTM for each kind of evaluated sensor is shown in Figure  6. The complete source code of Bulldozer is published on Github 2. A Python package of Bulldozer is available on Pypi 3  so that anyone can install it quickly. An exhaustive API has been developed, covering all the main steps of Bulldozer which can be called in standalone (e. g. pits filling) without having to execute the full pipeline.
Interfaces In order to reach a maximum number of users we have developed several interfaces for executing Bulldozer. Firstable, for the end users, a QGIS plugin has been published to allow users who do not have a computer science background to use the tool through QGIS GUI. Then a Command Line Interface (CLI) has been designed to run Bulldozer in a terminal. It works with a configuration file or by directly overriding the input parameters as CLI arguments. Finally, a Python API has been set up to allow Bulldozer to be used within an existing tool or through a script. Like the CLI, it can be launched with a configuration file or by overriding the Bulldozer parameters as arguments. The interfaces have been thought to be retrocompatible and adapted for future changes, such as generating level maps (contour each 10m for example).
Programming Language The Bulldozer pipeline was developed in Python even if the performances of this language are quite low compared to the majority of languages. This choice facilitates the arrival of new contributors and new users in the geoscience and remote sensing field. However, the core functions which perform the computational parts are developed in Cython in order to profit from the C++ performance in a Python context. It results in a high performance code wrapped into a popular high level language that is simple to integrate or address.
Multiprocessing and Shared Memory implementation For the whole pipeline, each step is implemented using a tiling strategy with the right stable margins. Each padded tile is processed independently using the multiprocessing feature Pro-cessPoolExecutor available in Python concurrent futures module. This feature could cause problems concerning the memory consumption when being applied to big numpy arrays. Indeed, the forking process strategy of Linux operating systems can lead to a duplication of allocated resources from the parent process to the forked child. Indeed, these large-scale missions do not directly produce the global DSMs but tiles that, when combined, cover large areas. The challenge consists in updating parts of regions that have been already generated and in particular preserving consistency and continuity on the borders and overlap areas of the new tiles.
) or MicMacRupnik et al. (2017) from Spot6, Pléiades and Pléiades Néo perfect sensor images and LiDAR-HD DSMs provided by IGN. Bulldozer is an open source tool that can be installed via pip from PyPi and the code can be retrieved on github. The code is written in Python and Cython and exposes an exhaustive API to access to all the methods of the pipeline. Finally, a QGIS plugin has been developed and made available.
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLVIII-4/W7-2023 FOSS4G (Free and Open Source Software for Geospatial) 2023 -Academic Track, 26 June-2 July 2023, Prizren, Kosovo This contribution has been peer-reviewed. https://doi. org/10. 5194/isprs-archives-XLVIII-4-W7-2023-89-2023 | © Author(s) 2023. CC BY 4. 0 License.
In this study, to understand the importance of these four early-to mid-Holocene ancient samples in the chronology of coastal adaptation, we first established their relationship to contemporary dolphins. We sequenced an ancient dolphin genome, SP1060, at 3x effective coverage (i. e. post quality control filtering, repeat masking, removing duplicates, base quality recalibration), to compare with a dataset of 60 contemporary genomes  19. We sequenced an additional three ancient dolphin genomes at ultra-low effective coverage (<0. 05x; Supplementary Table  1 ) to verify whether SP1060 was representative of the genetic variation within the ancient population, rather than, for example, a rare admixed individual. We show that parallel adaptation occurred rapidly after the emergence of new coastal habitats.
WNA Due to the fragmented and damaged nature of ancient DNA, reads with non-reference alleles are less likely to be mapped than those with reference alleles, creating reference bias. To reduce reference bias, we ran the multivariate analyses with the genomic data mapped to two references genomes: (i) the common bottlenose dolphin reference genome (GenBank: GCA_001922835. 1), which is a coastal western North Atlantic (WNA) individual, with relaxed parameters in BWA  28, 29  and (ii) the killer whale reference genome (GenBank GCA_000331955. 2)  30. Using relaxed parameters or mapping to another species should help to include a better representation of alternate alleles. We also ran all analyses both including and excluding transitions, as C-T transitions are in excess at molecule ends of the SP1060 sequence reads, reflecting post-mortem deamination damage (see details in the Methods section).
Data processing Read trimming. We used Illumina's CASAVA 1. 8. 2 software for the four ancient samples and the three new contemporary samples to convert Illumina's *. bcl files to fastq and perform demultiplexing allowing for no mismatch in the 6-nucleotide indices used for barcoding. For the ancient samples, we processed sequencing reads within the generated fastq files with ADAPTER-REMOVAL v. 2  57  to trim residual adapter sequence contamination and to remove adapter dimer sequences as well as low-quality stretches at 3´ends (i. e. consecutive stretches of N's and of bases with a quality score of 2 or lower). We discarded sequence reads that were ≤30 bp following trimming. For the 57 contemporary samples from ref. 19 and the three new contemporary samples generated during this study, we trimmed sequencing reads using Trimmomatic v.
0. 32  58  using default parameters, and we discarded sequence reads shorter than 75 bp-see details in ref. 19. Mitochondrial genomes. We first mapped the filtered reads to a modified version of the bottlenose dolphin mitochondrial genome from Scotland (GenBank KF570351. 1) 59 as per ref. 60. For the contemporary samples, we used BWA (v. 0. 7. 15) mem and default parameters  61. For the ancient samples, we used BWA aln with the seed disabled (-l 1024) to include reads with post-mortem damage at the read ends  29. We also mapped the reads to two additional individuals, to ensure there was no reference bias, which can be an issue with ancient DNA, i. e. one individual from the Black Sea (GenBank KF570326. 1) and one individual from the western North Atlantic (GenBank KF570378. 1)  59. We compared the sequences and checked they were the same.
For the ancient samples, we then mapped these reads to the reference bottlenose dolphin genome assembly (GenBank: GCA_001922835. 1, NIST Tur_tru v1) using BWA (v. 0. 7. 15) aln  61  with the seed disabled (-l 1024) and both default and adjusted parameters which were shown to reduce reference bias and include a better representation of alternate alleles  28. Those parameters were an edit distance (n) of 0. 01 and a maximum number of gaps (o) of 2. To further evaluate reference bias, we also mapped the reads of the ancient and contemporary samples to the killer whale genome assembly (GenBank GCA_000331955. 2)  30  using default parameters in BWA. Then, we kept only the mapped reads with a mapping quality of at least 25. We removed repeated regions as identified using RepeatMasker  66  , regions of excessive coverage and mapping artefacts (sites which were out of Hardy Weinberg Equilibrium with negative inbreeding coefficients in all populations as they could be paralogues or other mapping artefacts), and the sex chromosomes using a combination of bedtools v.
We ran all downstream analyses both including all sites and only including transversions. Results were the same, unless otherwise specified. We detected no heterozygous sites in the mitochondrial genomes of the ancient samples, indicating an absence of contamination from present-day DNA. Mitochondrial DNA phylogeny Time-calibrated models using the mtDNA. We first aligned mitochondrial genomes of T. truncatus, extracted from carbon-dated ancient subfossils (n = 4), contemporary samples (n = 70; including the 57 samples from ref. 19, three newly sequenced ones plus ten samples from Nykänen et al. (  2019 )  20  , see supplementary data on the DataSuds repository), or downloaded from GenBank (n = 68, see Supplementary Table  3  and Supplementary Data)  59  in software MEGA X 65 with a T. aduncus reference mitogenome (KF570335.
truncatus haplotypes from both trees using the R-function 'cophenetic' from ape R package v. 5. 0 73 in R v4. 0. 5. These sequences represented a sample obtained from coastal western North Atlantic (sample WNAC8) and sample 117696; and sample WNAC8 and a sample originating from the eastern Mediterranean Sea (sample EMED6). To estimate time-calibrated phylogenies, we used a two-step methodology by ref. 60. The purpose of the first step (delphinid tree model) was to estimate a calibration point (divergence of the two most evolutionarily distant T. truncatus haplotypes) for the second step (T. truncatus tree model). The two steps are described below. Delphinid phylogeny. We aligned thirteen protein-coding genes of the mitochondrial genome from the two most divergent T. truncatus haplotypes (WNAC8 and 117696, or WNAC8 and EMED6) with the gene sequences of 19 delphinid species downloaded from GenBank (Supplementary Table  3 )  59, 60, [74] [75] [76] [77] [78].
We used the 'greedy' search in Parti-tionFinder (v1. 1. 0)  79  to find the best partitioning scheme for each gene, and we built two time-calibrated phylogenetic trees (one with and one without the sample 117696) in BEAST2 v. 2. 6  80  with three different data partitions for nucleotide substitution models (Supplementary Table  4 ). We used all codon positions and only the third codon positions, both with a single strict clock model based on the results from a previous study  20  , and concatenated the individual gene trees into a single phylogeny to find the time to a most recent common ancestor (TMRCA) between the two most divergent T. truncatus haplotypes. We ran two independent models for each scenario with 40,000,000 MCMC steps, 10% pre-burn-in, and a sampling frequency of 4000. We used the divergence between Monodontidae and Delphinidae (mean = 10.
08 Myr, SD = 1. 413  81  ) to calibrate the root of the tree and we used a Calibrated Yule prior for the branching rate. We checked the convergence of MCMC chains and the Effective Sample Size (ESS) values relating to the model parameters in Tracer v. 1. 7. 1  82. After verifying convergence, we used LogCombiner v. 2. 6 and TreeAnnotator v. 2. 6  83  to combine and summarise the trees, respectively. T. truncatus phylogeny. We extracted 13 protein-coding gene regions from the 142 common bottlenose dolphin samples in order to estimate the coalescence times of different T. truncatus clades. We removed duplicate haplotypes from this dataset, thus eliminating one ancient sample, NMR2273 (duplicate with another ancient haplotype SP1060), and 51 contemporary samples. We determined the best nucleotide partitioning scheme for the remaining 90 haplotypes using Parti-tionFinder (v1.
44 software (http://tree. bio. ed. ac. uk/). Population structure. We used genotype likelihoods or pseudohaploid data in ANGSD v. 0. 921  25  to take into account differences in coverage between samples, where possible, or based our analyses on allele frequencies from called genotypes. We generated a vcf file including the contemporary individuals and SP1060 following the filtering steps as in ref. 19 and given in the supplementary scripts. In addition, in the vcf file, we kept only sites with data with 90% of the individuals and sites with no missing data in the ancient sample. Projection of the ancient samples on the principal components (PC). Note that we ran all the analyses mapping to the common bottlenose dolphin reference genome with relaxed BWA parameters and to the killer whale reference genome to check there was no effect of reference bias.
Results were the same, unless otherwise specified. We performed PCA projections of the ancient genomes on the principal components segregating the contemporary genome with smartpca from the eigensoft v. 7. 2. 0 package 24 using the option lsqproject. We ran smartpca both on (i) the contemporary samples and SP1060 and diploid genotype calls, including the contemporary individuals and (ii) SP1060 and the three other ancient samples using pseudo-haploid genotypes generated in ANGSD v. 0. 921. We converted the vcf genotype file to the eigenstrat format using the utility vcf2eigenstrat. py from https://github. com/mathii/gdc/blob/ master/vcf2eigenstrat. py. For the pseudo-haploid genotype calls, we first used the following filters in ANGSD, including a MAF of 0. 05 (minMinor 6) and data in 25% of the individuals (maxMis16): -dohaplocall 1 -doCounts 1 -minMapQ 25 -minQ 30 -C 50 -baq 1 -minMinor 6 -maxMis 16 -skipTriallelic 1 -remo-ve_bads 1 -uniqueOnly 1 -ref ref.
fa. We then used the utility haplotoPlink from ANGSD to transform the haplo file to Plink tped and tfam. We used Plink v. 1. 9 to transform those into bed/bim/fam files which we converted into eigenstrat using convertf from eigensoft v. 6. 1. 3. We used the options-allow-extrachr-chr-set 85 (killer whale reference genome scaffolds >10 Mbp) or 56 (bottlenose dolphin reference genome scaffolds >10 Mbp) in Plink. Factorial analysis (tfa). We also ran the tfa method, which takes drift into account  26  on the diploid genotypes only, including the contemporary individuals and SP1060 and sites with no missing data in SP1060, mapped to the bottlenose dolphin reference genome with relaxed parameters. We imputed missing values for the contemporary samples using the R package LEA v. 3. 2. 0 for the run with the highest likelihood for K = 6 (this value was previously described as the bestfitted number of populations  19  ) using the 'impute' function  86.
We also adjusted the genotypic data for coverage, as described in the method using the function "coverage_adjust" of the tfa R package, which uses a latent factor regression model. We used the function choose_lambda() to decide the lambda value for which the effect of sample age was removed from the fifth factor of the tfa analysis, similar values were obtained when choosing smaller factor values. We ran the tfa for K = 5 as the two pelagic Atlantic populations are very closely related. We also estimated ancestry coefficients for SP1060 including iteratively all populations. SP1060 did not show any ancestry relationship with the Pacific populations. Therefore, we estimated the ancestry coefficients for SP1060 including ENAc, WNAc and ENAp populations (including WNAp instead of ENAp gave the same results, likely due to the two populations being closely related).
We excluded the Pacific populations from this analysis as there are many other populations in between the regions. We only included the already published ENAc individuals (n = 10) and did not include the newly generated three individuals as they belong to different populations  51. We used a heuristic search algorithm, qpBrute (https://github. com/ekirving/qpbrute)  35, 36  to explore the space of all possible admixture graphs fitted to the four Atlantic populations using qpGraph. We built the admixture graph using two datasets: (i) pseudo-haploid data including SP1060 and the contemporary Atlantic populations mapped to the killer whale reference as it minimises reference bias, and (ii) called genotypes for the contemporary Atlantic populations mapped to the bottlenose dolphin reference. We used these two datasets to check results were similar, irrespective of the data type (pseudo-haploid and called genotypes) and the reference genome.