The effects of ice and hillslope erosion and detrital transport on the form of detrital thermochronological age probability distributions from glacial settings
Bibliographic record
Abstract
The impact of glaciers on the Quaternary evolution of mountainous landscapes remains controversial. While in-situ low-temperature thermochronology offers insights on past rock exhumation and landscape erosion, it also suffers from biases due to the difficulty of sampling bedrocks buried under the ice of glaciers. Detrital thermochronology attempts to bypass this issue by sampling sediments at, e.g. the catchment outlet, that may originate from beneath the ice. However, the age distribution resulting from detrital thermochronology does not only inform on the catchment exhumation, but also on the patterns and rates of surface erosion and sediment transport. In this study, we use a new version of a glacial landscape evolution model, iSOSIA to address the role of erosion and sediment transport by the ice on the form of synthetic detrital age distributions and thus, for inferred catchment erosion from such data. Sediments are tracked as Lagrangian particles that can be formed by bedrock erosion, transported by ice or hillslope processes and deposited. We apply our model to the Tiedemann glacier (British Columbia, Canada), which has simple morphological characteristics, such as a straight form and no connectivity with large tributary glaciers. Synthetic detrital age distributions are generated by specifying an erosion history, then sampling sediment particles at the frontal moraine of the modelled glacier. The detrital ages are represented as synoptic probability density functions (SPDFs). A characterization of sediment transport shows that 1500 years are required to reach an equilibrium for detrital particles age distributions, due to the large range of particle transport times from their sources to the frontal moraine. Second, varying sampling locations and strategies at the glacier front lead to varying detrital SPDFs, even at equilibrium. These discrepancies are related to (i) the selective storage of a large proportion of sediments in small tributary glaciers and in lateral moraines, (ii) the large range of particle transport times, due to varying transport lengths and to a strong variability of glacier ice velocity, (iii) the heterogeneous pattern of erosion, (iv) the advective nature of glacier sediment transport along ice streamlines that leads to a poor lateral mixing of particle detrital signatures inside the frontal moraine. Third, systematic comparisons between (U-Th)/He and fission track detrital ages, with different age-elevation profiles and relative age uncertainties, show that (i) the age increasing rate with elevation largely controls the ability to track sediment sources, and (ii) qualitative first-order information about distribution of erosion may still be extracted from thermochronological system with high variable uncertainties (> 30 %). Overall, our distributions in glaciated catchments are strongly impacted by erosion and transport processes and by their spatial variability. Combined with bedrock age distributions, detrital thermochronology can offer a means to constrain the transport pattern and time of sediment particles. However, results also suggest that detrital age distributions of glacial features like frontal moraines, are likely to reflect a transient case as the time required to reach detrital thermochronological equilibrium is of the order of the short-timescale glaciers dynamic variability, as little ice ages or recent glaciers recessions.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".