Interpreting exposure ages from ice‐cored moraines: a Neoglacial case study on Baffin Island, Arctic Canada
Bibliographic record
Abstract
ABSTRACT 10 Be dating of moraines has greatly improved our ability to constrain the timing of past glaciations and thus past cold events. However, the spread in ages from a single moraine is often greater than would be expected from measurement uncertainty, making paleoclimatic interpretations equivocal. Here we present 28 new 10 Be ages from ice‐cored Neoglacial moraines on Baffin Island, Arctic Canada, and explore the processes at play in moraine formation and evolution through field observations and a numerical debris‐covered glacier model. The insulating effect of debris cover modifies glacier lengths and results in the development of ice‐cored moraines over multiple advances and thousands of years. Although ice cores can persist for several millennia, spatially variable ice core melt‐out contributes to moraine degradation and boulder destabilization, making it likely that the 10 Be clock is reset on moraine boulders in these settings. Thus, exposure ages from ice‐cored moraines must be interpreted with caution. The oldest ages, after excluding samples with inheritance, provide the best estimates of initial moraine formation. Three Baffin Island moraines yield 10 Be ages suggesting formation at 5.2, 4.6 and 3.5 ka, respectively, adding to a growing body of evidence for significant summer cooling millennia before the Little Ice Age.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".