Early Results from KANG-GLAC: A Project to Understand Holocene Ice Sheet-Ocean Interaction and Marine Productivity in SE Greenland
Bibliographic record
Abstract
So far, melting of the Greenland Ice Sheet (GrIS) has been the biggest contributor from the Earth’s cryosphere to global sea-level rise. Major uncertainties remain about how oceanic heat is transported across the shelf and through the fjords to the faces of marine-terminating glaciers, and how this affects rates of ice melt and calving. In turn, the increasing supply of meltwater and nutrients to the ocean around Greenland is impacting marine ecosystems as primary productivity rises, subsequently increasing the potential for carbon to be buried as “blue carbon” in Greenland’s fjords as warming continues. In July-August 2024, the UK-funded KANG-GLAC project completed a 40-day multidisciplinary research cruise to SE Greenland where the 40-strong scientific party made a suite of integrated geological, ocean and biological observations. The main aims of the project are two-fold. First, it aims to better understand how marine-terminating glaciers respond to oceanic heat on longer timescales (decades to centuries) by reconstructing glacier and ice-sheet behaviour during the Holocene and in particular during the climatic warm period of the Holocene Thermal Maximum. Second, the project will quantify nutrient cycling in the water column and uppermost seafloor sediments in order to improve our knowledge of marine ecosystem response to meltwater supply from the GrIS. The cruise on the UK’s premier polar research vessel, the RRS Sir David Attenborough, is the start of a 3.5 year project. Here, we will present an overview of our field observations in this past-to-future project and outline the plans for future data-driven modelling of the Greenland Ice Sheet.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".