Greenland glacial history and local geodynamic consequences
Bibliographic record
Abstract
Space-time reconstructions of the continental ice-sheets that existed at Last Glacial Maximum (LGM) have previously been produced using two entirely independent methodologies. One based upon the use of theoretical models of ice-sheet accumulation and flow and one based upon the geophysical inversion of relative sea level (RSL) histories from previously ice-covered regions. The analyses described in this paper demonstrate the significant advantages that derive from the simultaneous application of both methods to the particular case of Greenland. We thereby show that the ICE-4G reconstruction of the glaciation history of this region from LGM to present, which was based upon the geophysical inversion of RSL data alone, was reasonably accurate in the peripheral regions where RSL data were available but inaccurate in the interior of the ice-sheet, which was unconstrained by such information. We test the new model of Greenland glacial history determined by the simultaneous application of the constraints that derive from ice-sheet modelling and the geophysical inversion of RSL data by employing recently published geodetic inferences of mass-balance over the entire interior region of the ice sheet and of GPS measurements of vertical crustal motion. These observations, which were not employed to constrain the ice-sheet reconstruction, provide significant support for the new glacial history for Greenland that our analyses have led us to infer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
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".