Synchronous acceleration of ice loss and glacial erosion, Glaciar Marinelli, Chilean Tierra del Fuego
Bibliographic record
Abstract
Abstract To contribute to the understanding of the influence of climate on glacial erosion and on orogenic processes, we report contemporary glacial erosion rates from a calving glacier in the Southern Andes and elucidate the influence of ice dynamics on erosion. Using seismic profiles of sediments collected in a proglacial fjord and a documented history of retreat, we determine the time-varying sediment flux of Glaciar Marinelli as a measure of basin-wide erosion rates, and compare these rates with the annual ice budget reconstructed using NCEP–NCAR reanalysis climate data from 1950 to 2005. The rate of erosion of the largest tidewater glacier in Tierra del Fuego averaged 39 ± 16 mm a−1 during the latter half of the 20th century, with an annual maximum approaching 130 mm a−1 following a decade of rapid retreat. A strong correlation emerges between the variable rate of ice delivery to the terminus and the erosion rate, providing quantitative insight into the relationship between ice fluxes and glacial erosion rates. For Glaciar Marinelli, as for other calving glaciers for which suitable data exist, the marked retreat and thinning over the past 50 years have resulted in a period of accelerated basal sliding and unusually rapid erosion.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".