Quaternary relief generation by polythermal glacier ice
Bibliographic record
Abstract
Abstract The juxtaposition of wet‐based erosive ice in valleys and cold‐based, non‐erosive ice atop felsenmeer‐covered interfluve plateaus has generated relief in the Torngat Mountains of northeastern Canada. Measurements of in situ terrestrial cosmogenic nuclide (TCN) concentrations from 31 bedrock sites, coupled with soils and geomorphology, indicate that erosion of the valleys has been >2 m during a single glacial–interglacial cycle. However, on summit plateaus the long‐term (over several glacial–interglacial cycles) erosion rate is <1·4 m Ma−1. TCN ratios reveal that the exposure plus ice‐cover history retained on some summit surfaces probably spans more than 800 ka despite complete ice cover as recently as 11 ka. A thermodynamic ice sheet model with a basal water calculation is used to calculate the sliding distance normalized by the duration of ice cover for the region. We formulate a general glacial erosion rule for the Torngat Mountains, which correlates TCN‐derived erosion rates for terrain once partially covered by cold‐based ice with modelled average ice basal sliding velocities. Erosion rates vary linearly with average sliding velocity by a glacial erosion coefficient of 5 × 10−7. Due to the significant distribution of cold‐based ice cover in this high latitude region, our estimates of net regional glacial erosion and glacial erosion coefficient are orders of magnitude lower than a previously published value. Copyright © 2005 John Wiley & Sons, Ltd.
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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) | 0.002 | 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".