Terrestrial cosmogenic nuclide bedrock depth profiles used to infer changes in Holocene glacier cover, Vintage Peak, southern Coast Mountains, British Columbia
Bibliographic record
Abstract
Abstract. The majority of glaciers in North America reached their maximum Holocene downvalley positions during the Little Ice Age (1300–1850 CE), and in most cases, this expansion also destroyed earlier evidence of glacier activity. Substantial retreat in the 20th and early 21st centuries exposed bedrock that fronts many glaciers that may record Early to mid-Holocene exposure and later burial by ice, which can be elucidated using multiple-nuclide cosmogenic surface exposure dating. Cores of bedrock allow the measurement of cosmogenic nuclide depth profiles to better constrain potential exposure and burial histories. We collected four bedrock surface samples for 10Be and 14C surface exposure dating and shallow (<0.6 m depth) bedrock cores from Vintage Peak, in the southern Coast Mountains of British Columbia, Canada. We apply a Monte Carlo approach to generate combinations of exposure and burial duration that can explain our data. We found that Vintage Peak became uncovered by the Cordilleran Ice Sheet between 14.5 and 9.7 ka, though upper elevations on Vintage Peak retained ice until 10–12 ka before retreating to smaller than modern positions. Glaciers on Vintage Peak advanced within 100 m of Late Holocene maximum positions around 4–6 ka. Poorly constrained subglacial erosion rates, possible inheritance, and variable mass shielding complicate our ability to more robustly interpret bedrock cosmogenic surface exposure histories. Nine 10Be ages on Late Holocene moraines reveal that glaciers reached their greatest Holocene extents by ca. 1300 CE. Our results agree with other regional glacier records and demonstrate the utility of surface exposure dating applied to deglaciated bedrock as a technique to help construct a record of Holocene glacier activity where organic material associated with glacier expansion may be absent or poorly preserved. Further work to increase exposure and/or burial history modeling complexity may help to better constrain complex exposure histories in glaciated alpine areas.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".