The effect of geothermal heat flux on the deglacial evolution of the Greenland ice sheet
Bibliographic record
Abstract
Geothermal heat flux (GHF) is known to be an important control on the basal thermal state of an ice sheet which, in turn, is a key factor in governing how the ice sheet will evolve in response to a given climate forcing. In recent years, several studies have estimated GHF beneath the Greenland ice sheet using different approaches (e.g. Rezvanbehbahani et al., Geophysical Research Letters, 2017; Martos et al., Geophysical Research Letters, 2018; Greve, Polar Data Journal, 2019). Comparing these different estimates indicates poor agreement and thus large uncertainty in our knowledge of this important boundary condition for modelling the ice sheet. The primary aim of this study is to quantify the influence of this uncertainty on modelling the past evolution of the ice sheet with a focus on the most recent deglaciation. We build on past work that considered three GHF models (Rogozhina et al., 2011) by considering over 100 different realizations of this input field. We use the uncertainty estimates from Martos et al. (Geophysical Research Letters, 2018) to generate GHF realisations via a statistical sampling procedure. A sensitivity analysis using these realisations and the Parallel Ice Sheet Model (PISM, Bueler and Brown, Journal of Geophysical Research, 2009) indicates that uncertainty in GHF has a dramatic impact on both the volume and spatial distribution of ice since the last glacial maximum, indicating that more precise constraints on this boundary condition are required to improve our understanding of past ice sheet evolution and, consequently, reduce uncertainty in future projections.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".