Seasonal changes in the viability and abundance of bacterial cells in the snowpack ecosystem of a High Arctic ice cap
Bibliographic record
Abstract
Microbes play an essential role in nutrient turnover within Arctic environments, and their contribution to biogeochemical cycles can depend on several factors, including but not limited to cell viability. In this study, we employed the SYBR-PI dual cell stain to epifluorescence microscopy to enumerate proportions of potentially viable and non-viable bacterial cell populations within a melting snowpack on an ice cap, Foxfonna in Svalbard. Non-viable cells dominated on Foxfonna (2.5 ± 0.36 × 107 cells m−2) during the June to early July period, when biological production was usually at its peak. Furthermore, non-viable cells also dominated the total cell abundance within superimposed ice (223 ± 242 cells mL−1) and glacial ice (695 ± 717 cells mL−1) beneath the snow. We propose that the rapid, early loss of cell viability was caused by a number of abiotic and biotic factors. Hence, necromass (dead cell residue) contributed to the export of organic matter to downstream ecosystems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".