Lichen-associated microbial members are prevalent in the snow microbiome of a sub-arctic alpine tundra
Bibliographic record
Abstract
Snow is the largest component of the cryosphere, with its cover and distribution rapidly decreasing over the last decade due to climate warming. It is imperative to characterize the snow (nival) microbial communities to better understand the role of microorganisms inhabiting these rapidly changing environments. Here, we investigated the core nival microbiome, the cultivable microbial members, and the microbial functional diversity of the remote Uapishka mountain range, a massif of alpine sub-arctic tundra and boreal forest. Snow samples were taken over a two-month interval along an altitude gradient with varying degree of anthropogenic traffic and vegetation cover. The core snow alpine tundra/boreal microbiome, which was present across all samples, constituted of Acetobacterales, Rhizobiales and Acidobacteriales bacterial orders, and of Mycosphaerellales and Lecanorales fungal orders, with the dominant fungal taxa being associated with lichens. The snow samples had low active functional diversity, with Richness values ranging from 0 to 19.5. The culture-based viable microbial enumeration ranged from 0 to 8.05 × 103 CFUs/mL. We isolated and whole-genome sequenced five microorganisms which included three fungi, one alga, and one potentially novel bacterium of the Lichenihabitans genus; all of which appear to be part of lichen-associated taxonomic clades.
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 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 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".