Taxonomic and metabolic profiling of glacial ice algal communities on the Greenland Ice Sheet
Bibliographic record
Abstract
During the summer melt season, glacier ice algal blooms are widespread in the area termed “dark zone” in the southwest region of Greenland. Due to their pigmentation, glacier ice algae reduce the albedo of the ice sheet, increasing surface melting. Despite their crucial role in the Greenland Ice Sheet (GrIS) ecosystem, we know little about their metabolic potential or functions. Here, we present insights into the links between microbial community composition using 18S and ITS2 sequencing and total metabolic profiling of samples dominated by glacier ice algae from the GrIS. Our analysis of ITS2 secondary structures reveals that blooms are dominated by a single algal species, yet glacier ice algal haplotype composition differs between sites (along a 40 km transect) and surface habitats (clean snow vs. high algal biomass ice). Furthermore, metabolic composition changes during the development of glacier ice algal blooms with an accumulation of fatty acids, although few differences were observed between sites along the transect. In addition, a few metabolites showed diurnal variations and our data suggest that under low light and freezing conditions, chlorophyll degradation, tocopherol abundance and phytol remobilization may be the key compounds changing in the glacier ice algae dominated samples. Overall, these results improve our understanding of the chemical environment in the GrIS supraglacial microbial community structure and the contribution of the primary producers dominated by the glacier ice algae. Our data also show that endo- and exo-metabolic patterns need to be differentiated and that multiplexed data sets will help gain a better insight into these complex algae- controlled ecosystems.
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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 | 0.001 | 0.001 |
| 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 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".