Aerobiology over the Southern Ocean – Implications for bacterial colonization of Antarctica
Bibliographic record
Abstract
Parts of the Antarctic are experiencing dramatic ecosystem change due to rapid and record warming, which may weaken biogeographic boundaries and modify dispersal barriers, increasing the risk of biological invasions. In this study, we collected air samples from 100 locations around the Southern Ocean to analyze bacterial biodiversity in the circumpolar air around the Antarctic continent, as understanding dispersal processes is paramount to assessing the risks of microbiological invasions. We also compared the Southern Ocean air bacterial biodiversity to non-polar ecosystems to identify the potential origin of these Southern Ocean air microorganisms. The bacterial diversity in the air had both local and global origins and presented low richness overall but high heterogeneity, compatible with a scenario whereby samples are composed of a suite of different species in very low relative abundances. Only 4% of Amplicon Sequence Variants (ASVs) were identified in both polar and non-polar air masses, suggesting that the polar air mass over the Southern Ocean can act as a selective dispersal filter. Furthermore, both microbial diversity and community structure both varied significantly with meteorological data, suggesting that regional bacterial biodiversity could be sensitive to changes in weather conditions, potentially altering the existing pattern of microbial deposition in the Antarctic.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".