Marine‐terminating glaciers sustain high productivity in Greenland fjords
Bibliographic record
Abstract
Accelerated mass loss from the Greenland ice sheet leads to glacier retreat and an increasing input of glacial meltwater to the fjords and coastal waters around Greenland. These high latitude ecosystems are highly productive and sustain important fisheries, yet it remains uncertain how they will respond to future changes in the Arctic cryosphere. Here we show that marine-terminating glaciers play a crucial role in sustaining high productivity of the fjord ecosystems. Hydrographic and biogeochemical data from two fjord systems adjacent to the Greenland ice sheet, suggest that marine ecosystem productivity is very differently regulated in fjords influenced by either land-terminating or marine-terminating glaciers. Rising subsurface meltwater plumes originating from marine-terminating glaciers entrain large volumes of ambient deep water to the surface. The resulting upwelling of nutrient-rich deep water sustains a high phytoplankton productivity throughout summer in the fjord with marine-terminating glaciers. In contrast, the fjord with only land-terminating glaciers lack this upwelling mechanism, and is characterized by lower productivity. Data on commercial halibut landings support that coastal regions influenced by large marine-terminating glaciers have substantially higher marine productivity. These results suggest that a switch from marine-terminating to land-terminating glaciers can substantially alter the productivity in the coastal zone around Greenland with potentially large ecological and socio-economic implications.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".