Quantifying Rates of Landscape Unzipping
Bibliographic record
Abstract
Abstract Measuring rates of valley head migration and determining the timing of canyon‐opening are insightful for the evolution of planetary surfaces. Spatial gradients of in situ‐produced cosmogenic nuclide concentrations along horizontal transects provide a framework for assessing the migration of valley networks and similar topographic features. We developed a new derivation for valley head retreat rates from the concentrations of in situ‐produced cosmogenic radionuclides in valley walls. The retreat rate is inversely proportional to the magnitude of the spatial concentration gradient and proportional to local nuclide production rates. By solving for a spatial gradient in concentration along a valley parallel transect, we created an expression for the explicit determination of valley head retreat, which we refer to herein as unzipping. We applied this expression to a seepage‐derived drainage network developing along the Apalachicola River, Florida, USA. Sample concentrations along a valley margin transect varied systematically from 2.9 × 105 to 3.5 × 105 atoms/g resulting in a gradient of 160 atoms/g/m, and from this value a valley head retreat rate of 0.025 m/y was found. The discrepancy between overall network age and current rates of valley head migration suggests intermittent network growth which is consistent with glacial‐interglacial precipitation variations during the Pleistocene. This method can be applied to a wide range of Earth‐surface environments. For the 10Be system, this method should be sensitive to unzipping rates bounded between 10−6 and 100 m/y.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".