Mapping variations in bedrock weathering with slope aspect under a sedimentary ridge-valley system using near-surface geophysics and drilling
Bibliographic record
Abstract
Understanding how soil thickness and bedrock weathering vary across ridge and valley topography is needed to constrain the flowpaths of water and sediment within a landscape. Here, we investigate how soil and weathered bedrock properties vary across a ridge-valley system in the Northern California Coast Ranges where topography varies with slope aspect such that north facing slopes, which are more densely vegetated, are steeper. In this study, we use seismic refraction surveys to extend observations made in boreholes and soil pits to the hillslope scale and identify that while soils are thicker on north facing slopes, the thickness of weathered bedrock does not vary with slope aspect. We estimate the porosity of the weathered bedrock and find that it is several times the annual rainfall, indicating that water storage is not limited by the available pore space, but rather the amount of precipitation delivered. Bedding-parallel and bedding-perpendicular seismic refraction surveys reveal weathering profiles that are thickest upslope and taper downslope to channels. We do not find a clear linear scaling relationship between depth to bedrock and hillslope length, which may be due to local variation in incision rate or bedrock hydraulic conductivity. Together, these findings, which suggest that the aspect-independent weathering profile structure is a legacy of past climate and vegetation conditions and that weathering varies strongly with hillslope position, have implications for hydrologic processes across this landscape.
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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 | 0.000 |
| 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 | 0.000 |
| 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".