The role of local-ice meltwater in the triggering of an under-ice phytoplankton bloom in an Arctic fjord
Bibliographic record
Abstract
We combined records from moorings, profilers, and CTD transects obtained in an Arctic fjord (Young Sound, Greenland) to assess the effects of local meltwater input at the beginning of ice melt while the fjord was still covered by ice. Results indicate that light penetrated below the ice and was available throughout the sampling period. Melting began at the mouth, where the ice and snow layers were thinner. At the mouth, meltwater triggered stratification and the onset of an under-ice phytoplankton bloom, as shown by an increase in chlorophyll-a (chl-a), fluorescence-CDOM, and oxygen saturation. Chl-a was highly correlated with salinity (−0.84) and temperature (0.88), indicating a strong association with the input of meltwater, while the maximums in chl-a and oxygen matched the distribution of the meltwater. At the mouth, in the area where the meltwater occurred, average chl-a increased from 0.27 mg m−3 to 0.40 mg m−3, and by the end of the record, it was three times greater than the surrounding waters. In the area of the patch of meltwater on 26–28 May, averaged oxygen increased by 4%–5% during the sampling period. Inside the fjord, patches of meltwater occurred and were advected from the mouth by an in-fjord flow. Within these patches of meltwater, chl-a and oxygen saturation increased, and by the end of the record, they were two times and 5% higher than the surrounding waters, respectively. This study shows that meltwater and stratification were more important than light for the onset of the under-ice bloom and suggests a significant portion of pelagic primary productivity occurs before ice breakup.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".