Identifying moraine surfaces with similar histories using lichen size distributions and the u2 statistic, southeast iceland
Bibliographic record
Abstract
.Moraine ridges are commonly used to identify past glacier ice margins and so infer glacier mass balance changes in response to climatic variability. However, differences in the form of past ice margins and post‐depositional modification of moraine surfaces can complicate these geomorphic records. As a result, simple relationships, such as distance from current ice margin, or linear alignments, may not necessarily indicate moraines deposited contemporaneously. These disturbances can also modify the size distribution of lichen populations, providing a distinctive signature for surfaces with similar histories and a means of identifying contemporaneous moraine surfaces. In this paper, statistical analysis of lichen size distributions is used to identify moraine surfaces with similar histories from complex suites of Little Ice Age moraine fragments in the proglacial areas of Skálafellsjökull (including Sultartungnajökull) and Heinabergsjökull, southeast Iceland. The analysis is based on a novel use of the goodness‐of‐fit statistic, Watson's U2 which provides a measure of ‘closeness’ between two sample distributions. Moraine fragments with similar histories are identified using cluster analysis of the U2 closeness values. The spatial pattern of the clustered moraines suggests three distinct phases of moraine deposition at Skálafellsjökull and Heinabergsjökull, four phases at Sultartungnajökull and a digitate planform margin at Heinabergsjökull. These spatial patterns are corroborated with tephrochronology. The success of the U2 statistical analysis in identifying surfaces with similar histories using lichen size distributions suggests that the technique may be useful in augmenting lichenometric surface dating as well as differentiating between other surfaces that support lichen populations, such as rock avalanche deposits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.000 | 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; both teacher heads agree on what is shown here.
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".