Melting glaciers stimulate large summer phytoplankton blooms in southwest Greenland waters
Bibliographic record
Abstract
Abstract Each summer, large quantities of freshwater and associated dissolved and particulate material are released from the Greenland Ice Sheet (GrIS) into local fjords where they promote local phytoplankton growth. Whether the influx of freshwater and associated micronutrients in glacial meltwater is able to stimulate phytoplankton growth beyond the fjords is disputed, however. Here we show that the arrival of freshwater discharge from outlet glaciers from both southeast and southwest GrIS coincides with large‐scale blooms in the Labrador Sea that extend over 300 km from the coast during summer. This summer bloom develops about a week after the arrival of glacial meltwater in early July and persists until the input of glacial meltwater slows in August or September, accounting for ~40% of annual net primary production for the area. In view of the absence of a significant change in the depth of the mixed layer associated with the arrival of glacial meltwater to the Labrador Sea, we suggest that the increase in phytoplankton biomass and productivity in summer is likely driven by a greater nutrient supply (most likely iron). Our results highlight that the ecological impact of meltwater from the GrIS likely extends far beyond the boundaries of the local fjords, encompassing much of the eastern Labrador Sea. Such impacts may increase if melting of the GrIS accelerates as predicted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.002 | 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 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".