Predicting sediment discharges and erosion rates in deep time—examples from the late Cretaceous North American continent
Bibliographic record
Abstract
Abstract Depositional stratigraphy represents the only physical archive of palaeo‐sediment routing and this limits analysis of ancient source‐to‐sink systems in both space and time. Here, we use palaeo‐digital elevation models (palaeoDEMs; based on high‐resolution palaeogeographic reconstructions), HadCM3L general circulation model climate data and the BQART suspended sediment discharge model to demonstrate a predictive, forward approach to palaeo‐sediment routing system analysis. To exemplify our approach, we use palaeoDEMs and HadCM3L data to predict the configurations, geometries and climates of large continental catchments in the Cenomanian and Turonian North American continent. Then, we use BQART to estimate suspended sediment discharges and catchment‐averaged erosion rates and we map their spatial distributions. We validate our estimates with published geologic constraints from the Cenomanian Dunvegan Formation, Alberta, Canada, and the Turonian Ferron Sandstone, Utah, USA, and find that estimates are consistent or within a factor of two to three. We then evaluate the univariate and multivariate sensitivity of our estimates to a range of uncertainty margins on palaeogeographic and palaeoclimatic boundary conditions; large uncertainty margins (≤50%/±5°C) still recover estimates of suspended sediment discharge within an order of magnitude of published constraints. PalaeoDEMs are therefore suitable as a first‐order investigative tool in palaeo‐sediment routing system analysis and are particularly useful where stratigraphic records are incomplete. We highlight the potential of this approach to predict the global spatio‐temporal response of suspended sediment discharges and catchment‐averaged erosion rates to long‐period tectonic and climatic forcing in the geologic past.
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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 | 0.000 | 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) | 0.000 | 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".