Seasonal Snowpack Microbial Ecology and Biogeochemistry on a High Arctic Ice Cap Reveals Negligible Autotrophic Activity During Spring and Summer Melt
Bibliographic record
Abstract
Abstract Snowpack ecosystem studies are primarily derived from research on snow‐on‐soil ecosystems. Greater research attention needs to be directed to the study of glacial snow covers as most snow cover lies on glaciers and ice sheets. With rising temperatures, snowpacks are getting wetter, which can potentially give rise to biologically productive snowpacks. The present study set out to determine the linkage between the thermal evolution of a snowpack and the seasonal microbial ecology of snow. We present the first comprehensive study of the seasonal microbial activity and biogeochemistry within a melting glacial snowpack on a High Arctic ice cap, Foxfonna, in Svalbard. Nutrients from winter atmospheric bulk deposition were supplemented by dust fertilization and weathering processes. NH 4 + and PO 4 3− resources in the snow therefore reached their highest values during late June and early July, at 22 and 13.9 mg m −2 , respectively. However, primary production did not respond to this nutrient resource due to an absence of autotrophs in the snowpack. The average autotrophic abundance on the ice cap throughout the melt season was 0.5 ± 2.7 cells mL −1 . Instead, the microbial cell abundance was dominated by bacterial cells that increased from an average of (39 ± 19 cells mL −1 ) in June to (363 ± 595 cells mL −1 ) in early July. Thus, the total seasonal biological production on Foxfonna was estimated at 153 mg C m −2 , and the glacial snowpack microbial ecosystem was identified as net‐heterotrophic. This work presents a seasonal “album” documenting the bacterial ecology of glacial snowpacks.
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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 | 0.000 |
| 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) | 0.000 | 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".