Limits to Reconstructing Paleotopography From Thermochronometer Data
Bibliographic record
Abstract
[1] Recent studies suggest that orogens can achieve a topographic steady state whereby equilibrium is reached between tectonics and erosion. However, steady state topography may not be the norm in many orogens experiencing large changes in climate or tectonics, which can produce topographic transients. The quantification of transient topography over geologic timescales requires reconstructing paleotopography, but this has proven difficult in many cases. This study investigates the utility of bedrock thermochronometer data to reconstruct orogen paleotopography over million year timescales. Apatite (U-Th)/He and fission track ages are integrated with a thermokinematic model for a single-parameter inversion of paleotopography. An iterative scheme is used that minimizes the misfit between predicted and observed cooling ages to identify the range of paleotopographies that could produce observed ages within sample uncertainty. Two approaches are considered. First, synthetic 2-D topographies are used to test the robustness of the approach. The following topographic evolution scenarios are considered: (1) lateral ridge migration, (2) topographic relief change, and (3) valley widening and deepening from glaciation. Second, the method is applied in three dimensions to existing data from the Coast Mountains of British Columbia, Canada. Results from both applications of the model suggest that (1) paleotopographic reconstruction will typically underpredict the magnitude of topographic change, especially relief change; (2) paleotopography is most successfully reconstructed after lateral ridge migration in long-wavelength topographies; and (3) reconstructed paleotopography from the Coast Mountains, British Columbia, suggests that glacial erosion may have the potential to remove drainage divides and laterally shift topographic ridges and peaks.
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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 | 0.008 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".