Metabolic diversity and adaptation of carbon-fixing microorganisms in extreme glacial cryoconite
Bibliographic record
Abstract
Abstract Understanding the diversity and functionality of carbon-fixing microorganisms in glacial ecosystems is crucial for elucidating carbon cycling processes in extreme environments. This study investigates the composition, diversity, and metabolic potential of carbon-fixing microorganisms in Tibetan cryoconite. Through metagenomic sequencing, we identified 13 carbon-fixing metagenome-assembled genomes spanning ten known and three unclassified genera. Deoxyribonucleic acid -stable isotope probing experiments with 13C-labeled sodium bicarbonate confirmed the metabolic activity of key genera, including Cyanobacteria (Microcoleus and Phormidesmis) and Proteobacteria (Rhizobacter and Rhodoferax). Our results reveal a diverse array of carbon fixation pathways, with the Calvin–Benson–Bassham cycle and 3-hydroxypropionate bicycle being the most prominent. In addition to photoautotrophic microorganisms, chemoautotrophic microorganisms also contribute to carbon fixation through mechanisms such as sulfur oxidation and atmospheric reducing gas utilization. The study highlights the adaptability of microbial communities to varying environmental conditions, including fluctuations in oxygen, light, and substrate availability. The findings underscore the complex interplay between carbon fixation pathways and environmental factors in cryoconite ecosystems. It also emphasizes the importance of exploring alternative carbon fixation pathways to gain a more comprehensive understanding of carbon cycling in these harsh and dynamic ecosystems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".