Ice nucleation activity of bacteria isolated from snow compared with organic and inorganic substrates
Bibliographic record
Abstract
Environmental context. Biological ice nucleators have been found to freeze water at very warm temperatures. The potential of bio-aerosols to greatly influence cloud chemistry and microphysics is becoming increasingly apparent, yet detailed knowledge of their actual role in atmospheric processes is lacking. The formation of ice in the atmosphere has significant local, regional and global influence, ranging from precipitation to cloud nucleation and thus climate. Ice nucleation tests on bacteria isolated from snow and laboratory-grown bacteria, in comparison with those of known organic and inorganic aerosols, shed light on this issue. Abstract. Ice nucleation experiments on bacteria isolated from snow as well as grown in the laboratory, in comparison with those of known organic and inorganic aerosols, examined the importance of bio-aerosols on cloud processes. Snow samples were collected from urban and suburban sites in the greater Montreal region in Canada (45°28'N, 73°45'W). Among many snow bacterial isolates, eight types of bacterial species, none belonging to known effective ice nucleators such as Pseudomonas or Erwinia genera, were identified to show an intermediate range of ice nucleation activity (–12.9 ± 1.3°C to –17.5 ± 2.8°C). Comparable results were also obtained for molten snow samples and inorganic suspensions (kaolin and montmorillonite) of buffered water solutions. The presence of organic molecules (oxalic, malonic and succinic acids) had minimal effect (<2°C) on ice nucleation. Considering experimental limitations, and drawing from observation in snow samples of a variety of bacterial populations with variable ice-nucleation ability, a shift in airborne-species population may significantly alter glaciation processes in clouds.
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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) | 0.010 | 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".