Late Holocene fluctuations of Stoppani Glacier, southernmost Patagonia
Bibliographic record
Abstract
Abstract Some lateral moraines contain a rich record of Holocene glacial expansion. Previous workers have used such evidence to document glacial fluctuations in western Canada, Alaska, and the U.S. Pacific Northwest, but similar studies in Patagonia are uncommon. Here we report on the late Holocene behavior of Stoppani Glacier, a 75 km2 glacier sourced in the Cordillera Darwin, southernmost Patagonia. Based on radiocarbon-dated wood and organic material contained in the glacier's northeast lateral moraine, we infer that Stoppani Glacier advanced shortly before 3.8–3.6, at 3.2–2.8, 2.3–2.1, and 0.3–0.2, and possibly sometime before 1.4–1.3 and 0.8–0.7 cal ka BP. These advances culminated at 0.3–0.2 cal ka BP, when the glacier constructed a prominent end moraine, marking its greatest extent of the past 4000 years. Although the timing of several of the advances overlap with the age range of glacial expansion recognized elsewhere in Patagonia, some do not. Asynchronous behavior observed in the glacial record may arise from the type of evidence (e.g., lateral stratigraphy vs. end moraine) used to document glacial fluctuations or variations in climate or glacial response times. A significant difference between the Stoppani record and some other Patagonian records is that the former indicates general expansion of ice over the last 4000 years, whereas the latter indicate a net decrease in extent over that period.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".