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Protein-level assembly increases protein sequence recovery from metagenomic samples manyfold
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Abstract
The open-source de novo protein-level assembler, Plass ( https://plass.mmseqs.com ), assembles six-frame-translated sequencing reads into protein sequences. It recovers 2–10 times more protein sequences from complex metagenomes and can assemble huge datasets. We assembled two redundancy-filtered reference protein catalogs, 2 billion sequences from 640 soil samples (soil reference protein catalog) and 292 million sequences from 775 marine eukaryotic metatranscriptomes (marine eukaryotic reference catalog), the largest free collections of protein sequences. The protein-level assembler can assemble protein catalogs from raw metagenomic sequencing data, enabling large-scale metagenomics studies.
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“The BFD was created in the following manner: “by clustering 2.5 billion protein sequences from Uniprot/TrEMBL+Swissprot 35 , Metaclust 36 and Soil Reference Catalog Marine Eukaryotic Reference Catalog assembled by Plass 37 .”
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“signs are there that it might be a workable approach for placing novel genetic diversity in a phylogenomic framework [84,85].”
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“For scalability, learning from sequences is important: while there are no central databases of high-throughput functional measurements, and few compilations exist, billions of sequences are available to learn from in sequence databases [47, 48].”
BackgroundMMseqs2 desktop and local web server app for fast, interactive sequence searches
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Self-citationLevy Karin, E., Mirdita, M., & Söding, J. (2020) · Microbiome · doi:10.1186/s40168-020-00808-x · Cited by 299
“We next constructed a comprehensive set of reference 263 proteins by uniting over 21,000,000 representative sequences of the Uniclust50 database [39], 264 the MERC dataset of over 292,000,000 protein sequence fragments assembled from 265 eukaryotic Tara Oceans metatranscriptomic datasets [40], and over 18,500,000 prot…”
MethodSelf-citationTransformer protein language models are unsupervised structure learners
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Verrucomicrobia use hundreds of enzymes to digest the algal polysaccharide fucoidan
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AF2Complex predicts direct physical interactions in multimeric proteins with deep learning
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Bilingual language model for protein sequence and structure
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Self-citationPredictProtein - Predicting Protein Structure and Function for 29 Years
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“ProtBertSec predicts secondary structure in three states (helix, strand, other) using ProtBert (14) embeddings derived from training on BFD with almost 3 × 109 proteins (6,46).”
MethodSelf-citationPredicting the structure of large protein complexes using AlphaFold and Monte Carlo tree search
(2022) · doi:10.1038/s41467-022-33729-4 · Cited by 226
“The fourth MSA is created by searching the Big Fantastic Database[36] (BFD from https://bfd.”
MethodTransformer-based deep learning for predicting protein properties in the life sciences
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“It comprises 65.9M families covering more than 2B protein sequences and was built using UniProt, and a reference protein catalogue ( Steinegger et al., 2019 ), clustered to 30% sequence identity.”
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“The analysis promoted utilizing UniRef50 [17] over UniRef100 [17] and BFD [3, 18].”
MethodInfluentialA simple guide to de novo transcriptome assembly and annotation
Raghavan, V., Kraft, L., Mesny, F., & Rigerte, L. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab563 · Cited by 191
“Finally, a novel approach to recovering a protein data set from RNAseq data is presented in the tool PLASS [152] which directly scans short reads for ORFs and extends these into amino acid contigs by examining overlaps between the translations.”
Embeddings from deep learning transfer GO annotations beyond homology
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Accurate prediction of protein structures and interactions using a three-track neural network
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“Following the tutorial at https://github.com/deepmind/alphafold, we downloaded the model parameters and genetic databases including UniRef90 [46], MGnify [47], BFD [48], Uniclust30 [49], PDB70 [50] and PDB [44] to implement AlphaFold2 in the Tianhe-2 supercomputer.”
MethodUnifying the known and unknown microbial coding sequence space
Vanni, C., Schechter, M. S., Acinas, S. G., Barberán, A., Buttigieg, P. L., Casamayor, E. O., Delmont, T. O., Duarte, C. M., Eren, A. M., Finn, R. D., et al. (2022) · eLife · doi:10.7554/elife.67667 · Cited by 134
“Alternatives like proteinlevel assembly (Steinegger et al., 2019b) combined with exploring the assembly graphs’ neighborhoods (Brown et al.”
BackgroundSelf-citationZheng, W. H., Wuyun, Q., Li, Y., Zhang, C., Freddolino, L., & Zhang, Y. (2024) · Nature Methods · doi:10.1038/s41592-023-02130-4 · Cited by 138
“The BFD is an HHblits-style HMM database that was created by clustering 2.5 billion protein sequences from UniProtKB 29 , Metaclust 31 , soil reference catalog and marine eukaryotic reference catalog assembled by Plass 36 .”
InfluentialScientific Large Language Models: A Survey on Biological & Chemical Domains
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InfluentialAMP‐BERT : Prediction of antimicrobial peptide function based on a BERT model
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“…fine-tuning task in our research, we utilize the Hugging Face Transformers API (Wolf et al., 2019) and ProtBERT-BFD, a BERT-based model that was pre-trained with approximately 2.1 billion protein sequences from the Big Fantastic Database (BFD) (Steinegger & Söding, 2018; Steinegger et al., 2019).”
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“To construct MSAs for each protein in the Se proteome, we initially used HHblits 44 to search against UniRef (2022 release 46 ) and BFD (2019 release 45 ).”
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Bryant, P., & Noé, F. (2024) · Nature Communications · doi:10.1038/s41467-024-51507-2 · Cited by 105
“This process constitutes the mainbottleneck for the predictions as very large databases suchas the Big Fantastic Database 25,26 are searched, which is very time-consuming 27 .”
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BackgroundMethodContrastive learning on protein embeddings enlightens midnight zone
(2022) · doi:10.1093/nargab/lqac043 · Cited by 105
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BackgroundWang, L., Patena, W., Baalen, K. A. V., Xie, Y., Singer, E. R., Gavrilenko, S., Warren-Williams, M., Han, L., Harrigan, H. R., Hartz, L. D., et al. (2023) · Cell · doi:10.1016/j.cell.2023.06.008 · Cited by 102
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Wang, R., Jin, J., Zou, Q., Nakai, K., & Wei, L. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac352 · Cited by 91
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Camargo, A. P., de Souza, R. S. C., José, J., Gerhardt, I. R., Dante, R. A., Mukherjee, S., Huntemann, M., Kyrpides, N. C., Carazzolle, M. F., & Arruda, P. (2022) · The ISME Journal · doi:10.1038/s41396-022-01345-1 · Cited by 92
“c7e35) [92], using the trimmed read pairs as input.”
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“…are an exciting breakthrough in protein sequence modeling as they were shown to capture complex dependencies between protein residues 23 based solely on training using large, but unlabeled, protein sequence databases 24,25 (rather than sequences belonging to a specific protein family or task).”
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MethodNovel machine learning approaches revolutionize protein knowledge
Bordin, N., Dallago, C., Heinzinger, M., Kim, S., Littmann, M., Rauer, C., Steinegger, M., Rost, B., & Orengo, C. (2023) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2022.11.001 · Cited by 72
“1 billion sequences [63] or MGnify [64] with 2.”
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Moffat, L., & Jones, D. T. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab491 · Cited by 73
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Hu, R., Fu, L., Chen, Y., Chen, J., Qiao, Y., & Si, T. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac570 · Cited by 71
Youngblut, N. D., & Ley, R. E. (2021) · PeerJ · doi:10.7717/peerj.12198 · Cited by 64
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“…2019b), Mgnify (Mitchell et al., 2020), UniProt (UniPort Consortium, 2021), UniRef90 (Suzek et al., 2007), Uniclust30 (Mirdita et al., 2017), PDB70 (Steinegger et al., 2019a), PDB seqres (Berman et al., 2000), and PDB (Berman et al., 2000) to implement AlphaFold-Multimer (Evans et al., 2021,…”
Aguilar-Marín, S. B., Betancur-Murillo, C. L., Isaza, G. A., Mesa, H., & Jovel, J. (2020) · BMC Microbiology · doi:10.1186/s12866-020-02037-6 · Cited by 66
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Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models
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“The ProtTrans model family [65] introduced a series of transformer model architectures (Transformer-XL [244], BERT [102], and T5 [245]) pre-trained on protein sequences from UniRef [242] and the Big Fantastic Database [246].”
MethodChen, B., Cheng, X., Li, P., Geng, Y., Gong, J., Li, S., Bei, Z., Tan, X., Wang, B., Zeng, X., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02636-z · Cited by 216
“These models are trained on large-scale protein datasets 9–11 that encompass billions of sequences, allowing them to capture evolutionary patterns and sequence features that are inherent in protein structures.”
Metagenomic evidence of a novel family of anammox bacteria in a subsea environment
Suarez, C., Martins, P. D., Jetten, M. S. M., Karačić, S., Wilén, B., Modin, O., Hagelia, P., Hermansson, M., & Persson, F. (2022) · Environmental Microbiology · doi:10.1111/1462-2920.16006 · Cited by 55
“One approach that could be used is proteinlevel assembly (Steinegger et al., 2019).”
BackgroundProT-VAE: Protein Transformer Variational AutoEncoder for functional protein design
Sevgen, E., Moller, J., Lange, A., Parker, J., Quigley, S. D., Mayer, J., Srivastava, P., Gayatri, S., Hosfield, D. J., Dilks, C. M., et al. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2408737122 · Cited by 59
Singh, J., Paliwal, K. K., Litfin, T., Singh, J., & Zhou, Y. (2022) · Scientific Reports · doi:10.1038/s41598-022-11684-w · Cited by 59
“These methods were trained on extensive protein databases such as Uniref 23 , Uniclust 24 , Pfam 25 , and BFD 26,27 .”
MethodSETH predicts nuances of residue disorder from protein embeddings
Ilzhöfer, D., Heinzinger, M., & Rost, B. (2022) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2022.1019597 · Cited by 52
Deep learning methods for protein function prediction
Boadu, F., Lee, A., & Cheng, J. (2025) · PROTEOMICS · doi:10.1002/pmic.202300471 · Cited by 53
“TEMPROT [39] is another sequence-based protein function prediction method leveraging ProtBERT-BFD [40], a transformer language model pretrained on the BFD dataset [8, 41, 42 and a sequence-similarity search tool, BLASTp [43], was developed to improve the prediction performance.”
A text-guided protein design framework
Liu, S., Li, Y., Li, Z., Gitter, A., Zhu, Y., Lu, J., Xu, Z., Nie, W., Ramanathan, A., Xiao, C., et al. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01011-z · Cited by 97
“ProtBERT was pretrained on a large protein sequence corpus (Steinegger et al., 2019; Steinegger & Söding, 2018), and the pretraining task is to reconstruct the masked tokens.”
Protein tertiary structure prediction and refinement using deep learning and Rosetta in CASP14
Anishchenko, I. V., Baek, M., Park, H., Hiranuma, N., Kim, D. E., Dauparas, J., Mansoor, S., Humphreys, I. R., & Baker, D. A. (2021) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26194 · Cited by 50
Increasing the power of interpretation for soil metaproteomics data
Jouffret, V., Miotello, G., Culotta, K., Ayrault, S., Pible, O., & Armengaud, J. (2021) · Microbiome · doi:10.1186/s40168-021-01139-1 · Cited by 52
Singh, J., Litfin, T., Singh, J., Paliwal, K. K., & Zhou, Y. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac053 · Cited by 51
An analysis of protein language model embeddings for fold prediction
Villegas-Morcillo, A., Gomez, A. M., & Sanchez, V. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac142 · Cited by 46
Deep learning for protein secondary structure prediction: Pre and post-AlphaFold
Ismi, D. P., Pulungan, R., & Afiahayati (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.11.012 · Cited by 56
“Besides UniRef100, ProtBert and ProtXL are also trained using BFD [152].”
MethodBrown, C. T., Moritz, D., O’Brien, M. P., Reidl, F., Reiter, T. E., & Sullivan, B. D. (2020) · Genome biology · doi:10.1186/s13059-020-02066-4 · Cited by 46
“1.3 [31] and Plass v2-c7e35 [36], treating the reads as single-ended.”
BackgroundMethodInfluentialAre protein language models the new universal key?
Weißenow, K., & Rost, B. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.102997 · Cited by 54
Opportunities and Challenges of Data-Driven Virus Discovery
Lauber, C., & Seitz, S. (2022) · Biomolecules · doi:10.3390/biom12081073 · Cited by 45
“For instance, assembling based on the encoded protein sequence [39] would be an approach to counteract high variability at the nucleotide level, which otherwise leads to fragmentary contig sequences.”
BackgroundNallapareddy, V., Bordin, N., Sillitoe, I., Heinzinger, M., Littmann, M., Waman, V. P., Sen, N., Rost, B., & Orengo, C. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad029 · Cited by 47
Fenoy, E., Edera, A. A., & Stegmayer, G. S. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac232 · Cited by 47
Manfredi, M., Savojardo, C., Martelli, P. L., & Casadio, R. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac678 · Cited by 48
Zhu, Y., Liu, Z., Liu, Y., Ji, Z., & Yu, D. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae040 · Cited by 49
“…and ESM-MSA are both unsupervised attention networks with 36, 24 and 12 layers, respectively, and trained on Uniref50 [52], UniClust30 & Uniref50, and BFD (Big Fantastic Database) [53] & Uniref50 databases, respectively, where ‘&’ means that two databases are both used to train a transformer.”
MethodProtein sequence‐to‐structure learning: Is this the end(‐to‐end revolution)?
Laine, E., Eismann, S., Elofsson, A., & Grudinin, S. (2021) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26235 · Cited by 44
“They include databases like NCBI GenBank [66], Metaclust [67], BFD [68], MetaEuk [69], EBI MGnify [70], and IMG/M [71].”
MethodPrevalent bee venom genes evolved before the aculeate stinger and eusociality
Koludarov, I., Velasque, M., Senoner, T., Timm, T., Greve, C., Hamadou, A. B., Gupta, D. K., Lochnit, G., Heinzinger, M., Vilcinskas, A., et al. (2023) · BMC Biology · doi:10.1186/s12915-023-01656-5 · Cited by 44
“The protein Language Model (pLM) ProtT5 was trained solely on unlabelled protein sequences from BFD (Big Fantastic Database; 2.5 billion sequences including metagenomic sequences) [126] and UniRef50.”
MethodInfluentialScaling Unlocks Broader Generation and Deeper Functional Understanding of Proteins
Bhatnagar, A., Jain, S., Beazer, J., Curran, S. C., Hoffnagle, A. M., Ching, K., Martyn, M., Nayfach, S., Ruffolo, J. A., & Madani, A. (2025) · neural information processing systems · doi:10.52202/085713-1536 · Cited by 55
The OMG dataset: An Open MetaGenomic corpus for mixed-modality genomic language modelingPreprint
Cornman, A., West-Roberts, J. A., Camargo, A. P., Roux, S., Beracochea, M., Mirdita, M., Ovchinnikov, S., & Hwang, Y. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.14.607850 · Cited by 52
“Previous efforts to increase the diversity of the pretraining data includes cluster-balanced sampling (e.g. UniRef50/D for ESM models (Rives et al., 2021) and sequence identity-based clustering of compiled protein databases beyond curated databases (e.g. BFD (Steinegger et al., 2019; Elnaggar et al., 2022)).”
BackgroundSelf-citationFast, sensitive detection of protein homologs using deep dense retrieval
Hong, L., Hu, Z., Sun, S., Tang, X., Wang, J., Tan, Q., Zheng, L., Wang, S., Xu, S. Y., King, I., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-024-02353-6 · Cited by 46
Improved protein complex prediction with AlphaFold-multimer by denoising the MSA profile
Bryant, P., & Noé, F. (2024) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1012253 · Cited by 53
Nearest neighbor search on embeddings rapidly identifies distant protein relations
Schütze, K., Heinzinger, M., Steinegger, M., & Rost, B. (2022) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2022.1033775 · Cited by 41
“…and have more free parameters than SeqVec (ProtBert 420 M — with M for million, ProtAlbert 224M, ProtT5 3B — with B for billion, vs. SeqVec 93 M) and were trained on the much larger BFD dataset (UniRef50 50 M) (Steinegger and Söding, 2018; Steinegger et al., 2019; Elnaggar et al., 2020).”
BackgroundMethodInfluentialSelf-citationLee, S., Kim, S., Lee, G. R., Kwon, S., Woo, H., Seok, C., & Park, H. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.11.057 · Cited by 42
“Sequences were searched against the UniRef90 (version 2022) [34], BFD (version Mar 2019) [35], and Mgnify (version Dec 2018) [36] metagenomics databases using jackhmmer (version 3.3.2) [37].”
MethodInfluentialZero‐shot mutation effect prediction on protein stability and function using RoseTTAFold
Mansoor, S., Baek, M., Juergens, D., Watson, J. L., & Baker, D. A. (2023) · Protein Science · doi:10.1002/pro.4780 · Cited by 45
ParaFold: Paralleling AlphaFold for Large-Scale Predictions
Zhong, B., Su, X., Wen, M., Zuo, S.-C., Hong, L. Y., & Lin, J. (2022) · International Conference on High Performance Computing in Asia-Pacific Region Workshops (HPC Asia Workshops) · doi:10.1145/3503470.3503471 · Cited by 41
“Therefore, in batch inferences of N proteins, the compile time was thus reduced from 5N to 5 when computing 5 models, and 10N to 10 when computing 10 models. jackhmmer jackhmmer HHblits UniRef90 58 GB MGnify 64 GB BFD + Uniclust30 1.7 TB (a) Sequential Tasks on AlphaFold (b) Parallel Tasks on ParaFold 8 CPUs 8 CPUs 4…”
MethodInfluentialPakhrin, S. C., Pokharel, S., Aoki-Kinoshita, K. F., Beck, M. R., Dam, T. K., Caragea, D., & KC, D. B. (2023) · Glycobiology · doi:10.1093/glycob/cwad033 · Cited by 39
Kinch, L. N., Schaeffer, R. D., Kryshtafovych, A., & Grishin, N. V. (2021) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26202 · Cited by 39
Polonsky, K., Pupko, T., & Freund, N. T. (2023) · The Journal of Immunology · doi:10.4049/jimmunol.2300150 · Cited by 41
Kandathil, S. M., Greener, J. G., Lau, A. M., & Jones, D. T. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2113348119 · Cited by 48
Metadata
- Authors
- Steinegger, M., Mirdita, M., & Söding, J.
- Affiliations
- Seoul National University (KR)
- Max Planck Institute for Biophysical Chemistry (DE)
- Corresponding author
- Steinegger, Martin
- Söding, Johannes
- Venue
- Nature Methods
- Published
- 2019-06-24
- Volume
- 16
- Issue
- 7
- Pages
- 603-606
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W2949342052
- Cited by
- 445
- References
- 31
- Citations by year
- 2026: 38 · 2025: 78 · 2024: 101 · 2023: 78 · 2022: 71 · 2021: 50 · 2020: 23 · 2019: 6
- FWCI
- 12.42
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Methods (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Seoul National University Open Repository (Seoul National University) (submittedVersion)
- Funders
- Materials and Energy Research Center
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Physical Sciences › Environmental Science › Ecology › Environmental DNA in Biodiversity Studies
- Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
- Keywords
- metagenomics, metatranscriptomics, soil metagenome
- MeSH terms
- Amino Acid Sequence, Codon, Proteins, Open Reading Frames, Metagenomics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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