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Geoscientists use observations and descriptions of the rock record to study the origins and history of our planet, which has resulted in a vast volume of scientific literature. Recent progress in natural language processing (NLP) has the potential to parse through and extract knowledge from unstructured text, but there has, so far, been only limited work on the concepts and vocabularies that are specific to geoscience. Herein we harvest and process public geoscientific reports (i.e., Canadian federal and provincial geological survey publications databases) and a subset of open access and peer-reviewed publications to train new, geoscience-specific language models to address that knowledge gap. Language model performance is validated using a series of new geoscience-specific NLP tasks (i.e., analogies, clustering, relatedness, and nearest neighbour analysis) that were developed as part of the current study. The raw and processed national geological survey corpora, language models, and evaluation criteria are all made public for the first time. We demonstrate that non-contextual (i.e., Global Vectors for Word Representation, GloVe) and contextual (i.e., Bidirectional Encoder Representations from Transformers, BERT) language models updated using the geoscientific corpora outperform the generic versions of these models for each of the evaluation criteria. Principal component analysis further demonstrates that word embeddings trained on geoscientific text capture meaningful semantic relationships, including rock classifications, mineral properties and compositions, and the geochemical behaviour of elements. Semantic relationships that emerge from the vector space have the potential to unlock latent knowledge within unstructured text, and perhaps more importantly, also highlight the potential for other downstream geoscience-focused NLP tasks (e.g., keyword prediction, document similarity, recommender systems, rock and mineral classification).

ABSTRACT Features of the airway microbiome in persons with cystic fibrosis (pwCF) are correlated with disease progression. Microbes have traditionally been classified for their ability to tolerate oxygen. It is unknown whether supplemental oxygen, a common medical intervention, affects the airway microbiome of pwCF. We hypothesized that hyperoxia significantly impacts the pulmonary microbiome in cystic fibrosis. In this study, we cultured spontaneously expectorated sputum from pwCF in artificial sputum medium under 21%, 50%, and 100% oxygen conditions using a previously validated model system that recapitulates microbial community composition in uncultured sputum. Culture aliquots taken at 24, 48, and 72 h, along with uncultured sputum, underwent shotgun metagenomic sequencing with absolute abundance values obtained with the use of spike-in bacteria. Raw sequencing files were processed using the bioBakery pipeline to determine changes in taxonomy, predicted function, antimicrobial resistance genes, and mobile genetic elements. Hyperoxia reduced absolute microbial load, species richness, and diversity. Hyperoxia reduced absolute abundance of specific microbes, including facultative anaerobes such as Rothia and some Streptococcus species, with minimal impact on canonical CF pathogens such as Pseudomonas aeruginosa and Staphylococcus aureus. The effect size of hyperoxia on predicted functional pathways was stronger than that on taxonomy. Large changes in microbial cooccurrence networks were noted. Hyperoxia exposure perturbs airway microbial communities in a manner well tolerated by key pathogens. Supplemental oxygen use may enable the growth of lung pathogens and should be further studied in the clinical setting. IMPORTANCE The airway microbiome in persons with cystic fibrosis (pwCF) is correlated with lung function and disease severity. Supplemental oxygen use is common in more advanced CF, yet its role in perturbing airway microbial communities is unknown. By culturing sputum samples from pwCF under normal and elevated oxygen conditions, we found that increased oxygen led to reduced total numbers and diversity of microbes, with relative sparing of common CF pathogens such as Pseudomonas aeruginosa and Staphylococcus aureus. Supplemental oxygen use may enable the growth of lung pathogens and should be further studied in the clinical setting.