Stress factors resulting from the Arctic vernal sea-ice melt
Bibliographic record
Abstract
During sea-ice melt in the Arctic, primary production by sympagic (sea-ice) algae can be exported efficiently to the seabed if sinking rates are rapid and activities of associated heterotrophic bacteria are limited. Salinity stress due to melting ice has been suggested to account for such low bacterial activity. We further tested this hypothesis by analyzing samples of sea ice and sinking particles collected from May 18 to June 29, 2016, in western Baffin Bay as part of the Green Edge project. We applied a method not previously used in polar regions—quantitative PCR coupled to the propidium monoazide DNA-binding method—to evaluate the viability of bacteria associated with sympagic and sinking algae. We also measured cis-trans isomerase activity, known to indicate rapid bacterial response to salinity stress in culture studies, as well as free fatty acids known to be produced by algae as bactericidal compounds. The viability of sympagic-associated bacteria was strong in May (only approximately 10% mortality of total bacteria) and weaker in June (average mortality of 43%; maximum of 75%), with instances of elevated mortality in sinking particle samples across the time series (up to 72%). Short-term stress reflected by cis-trans isomerase activity was observed only in samples of sinking particles collected early in the time series. Following snow melt, however, and saturating levels of photosynthetically active radiation in June, we observed enhanced ice-algal production of bactericidal compounds (free palmitoleic acid; up to 4.8 mg L–1). We thus suggest that protection of sinking sympagic material from bacterial degradation early in a melt season results from low bacterial activity due to salinity stress, while later in the season, algal production of bactericidal compounds induces bacterial mortality. A succession of bacterial stressors during Arctic ice melt helps to explain the efficient export of sea-ice algal material to the seabed.
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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 | 0.000 | 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 | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".