Using fill terraces to understand incision rates and evolution of the Colorado River in eastern Grand Canyon, Arizona
Bibliographic record
Abstract
The incision and aggradation of the Colorado River in eastern Grand Canyon through middle to late Quaternary time can be traced in detail using well‐exposed fill terraces dated by a combination of optically stimulated luminescence, uranium series, and cosmogenic nuclide dating. This fluvial history provides the best bedrock incision rate for this important landscape and highlights the complications and advantages of fill terrace records for understanding river long‐profile evolution and incision. The use of fill terraces, as distinct from strath terraces, for calculating incision rates is complicated by the cyclic alluviation and incision they record. In the example of the Grand Canyon this has led to various rates being reported by different workers and rates that tend to be overestimates in shorter records. We illustrate that a meaningful long‐term bedrock incision rate of ∼140 m/m.y. can be extracted from the Grand Canyon record by linking episodes when the Colorado River is floored on bedrock. Variable incision rates reported in the greater region may be, to some degree, due to inconsistent calculations. Our data also highlight that the Colorado River has been a mixed alluvial‐bedrock river through both time and space and has been a bedrock river for less than half of its Pleistocene history. This strong temporal variation, combined with the varying bedrock the river encounters on its path, heightens the challenge of understanding the tectonic, climatic, and drainage integration controls on the form and evolution of the Colorado River's long profile.
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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 | 0.001 |
| 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) | 0.001 | 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".