Arctic microbial and next-generation sequencing approach for bacteria in snow and frost flowers: selected 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 diversity, freezing nucleation ability of the microbial communities of five different snow types and frost flowers. In addition to the culturing and gene-sequence-based identification approach, we utilized a state-of-the-art genomic next-generation sequencing (NGS) technique to examine the diversity of bacterial communities in Arctic samples. Known phyla or candidate divisions were detected (11–18) with the majority of sequences (12.3–83.1%) belonging to one of the five major phyla: Proteobacteria, Actinobacteria, Bacteroidetes, Firmicutes, and Cyanobacteria. The number of genera detected ranged from, 101–245. The highest number of cultivable bacteria was observed in frost flowers (FFs) and accumulated snow (AS) with 325 ± 35 and 314 ± 142 CFU m L−1, respectively; and for cultivable fungi 5 ± 1 CFU m L−1 in windpack (WP) and blowing snow (BS). Morphology/elemental composition and ice-nucleating abilities of the identified taxa were obtained using high resolution electron microscopy with energy-dispersive X-ray spectroscopy and ice nucleation cold-plate, respectively. Freezing point temperatures for bacterial isolates ranged from −20.3 ± 1.5 to −15.7 ± 5.6 °C, and for melted snow samples from −9.5 ± 1.0 to −18.4 ± 0.1 °C. An isolate belonging to the genus Bacillus (96% similarity) had ice nucleation activity of −6.8 ± 0.2 °C. Comparison with Montreal urban snow, revealed that a seemingly diverse community of bacteria exists in the Arctic with some taxa possibly originating from distinct ecological environments. We discuss the potential impact of snow microorganisms in the freezing and melting process of the snowpack in the Arctic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".