A next generation sequencing of Arctic bacteria in snow and frost flowers: identification, abundance and freezing nucleation
Bibliographic record
Abstract
Abstract. During the spring of 2009, as part of the Ocean–Atmosphere–Sea Ice–Snowpack (OASIS) campaign in Barrow, Alaska, USA, we examined the identity, population, freezing nucleation ability of the microbial communities of five different snow types and frost flowers. In addition to the conventional culture-based PCR identification approach, we deployed a state-of-the-art genomic Next Generation Sequencing (NGS) technique to examine diverse bacterial communities in Arctic samples. 11–18 known phyla or candidate divisions were identified with the great majority of sequences (12.3–83.1%) belonging to one of the five major phyla: Proteobacteria, Actinobacteria, Bacteroidetes, Firmicutes and Cyanobacteria. At the genus level, 101–245 different genera were detected. The highest number of cultivable bacteria in cultured samples was observed in frost flowers (FF) and accumulated snow (AS) with 325 ± 35 and 314 ± 142 CFU mL−1, respectively; and for cultivable fungi 5 ± 1 CFU mL−1 in windpack (WP) and blowing snow (BS). Complementary morphology and ice-nucleating abilities of the identified taxa were obtained using high resolution electron microscopy and ice nucleation cold-plate, respectively. Freezing point temperatures for bacterial isolate ranged from −20.3 ± 1.5 to −15.7 ± 5.6 °C, and for melted samples from 9.5 ± 1.0 to 18.4 ± 0.1 °C. An isolate belonging to the Bacillus species (96% similarity) had ice nucleation activity of −6.8 ± 0.2 °C. Comparison with Montreal urban snow, revealed a seemingly diverse community of bacteria exists in the Arctic with many originating from distinct ecological environments, and we discuss the potential impact of microbial snow in the freezing and melting process of the snowpack in the Arctic.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".