Prospecting for ice association: characterization of freeze–thaw selected enrichment cultures from latitudinally distant soils
Bibliographic record
Abstract
Freeze-thaw stress has previously been shown to alter soil community structure and function. We sought to further investigate this stress on enriched microbial consortia with the aim of identifying microbes with ice-associating adaptations that facilitate survival. Enrichments were established to obtain culturable psychrotolerant microbes from soil samples from the latitudinal extremes of the Canadian Shield plateau. The resulting consortia were subjected to consecutive freeze-thaw cycles, and survivors were putatively identified by their 16S rRNA gene sequences. Even though the northerly site was exposed to longer, colder winters and large spring-time temperature fluctuations, the selective regime similarly affected both enriched consortia. Quantitative PCR and metagenomic sequencing were used to determine the frequency of a subset of the resistant microbes in the original enrichments. The metagenomes showed 22 initial genera, only 6 survived and these were not dominant prior to selection. When survivors were assayed for ice recrystallization inhibition and ice nucleation activities, over 60% had at least one of these properties. These phenotypes were not more prevalent in the northern enrichment, indicating that regarding these adaptations, the enrichment strategy yielded seemingly functionally similar consortia from each site.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".