Ice‐active characteristics of soil bacteria selected by ice‐affinity
Bibliographic record
Abstract
As an initial screen for microorganisms that produce ice-active macromolecules, ice-affinity was used to select microorganisms from soil consortia originating from three temperate regions. Once selected and subsequently purified to single colonies, these microbes were putatively identified by 16S ribosomal RNA sequencing and assayed for various ice-active properties. Ice-affinity selection appeared to select for bacteria with ice-associating activities: inhibition of ice recrystallization; ice nucleation; ice shaping. Although none of these activities were observed in Paenibacillus amyloliticus C8, others such as Chryseobacterium sp. GL8, demonstrated both ice recrystallization inhibition and ice-shaping activities. Pseudomonas borealis DL7 was classified as a type I ice nucleator, Flavobacterium sp. GL7, was identified as a type III ice nucleator and Acinetobacter radioresistens DL5 demonstrated ice recrystallization inhibition. In all, 19 different culturable bacteria were selected from the thousands of microbes in late-summer collected soil samples. Many of the selected microbes have been previously reported in glacial ice cores or polar sea ice, and of five isolates that were further characterized, four showed ice-associating activities. These results indicate the significant potential of ice-affinity selection even with temperate climate soils, suggesting that sampling in more extreme and remote areas is not required for the isolation of ice-active bacteria.
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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 | 0.000 | 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) | 0.001 | 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".