Subsurface Weathering Revealed in Hillslope‐Integrated Porosity Distributions
Bibliographic record
Abstract
Abstract Subsurface weathering has traditionally been measured using cores and boreholes to quantify vertical variations in weathered material properties. However, these measurements are typically available at only a few, potentially unrepresentative points on hillslopes. Geophysical surveys, conversely, span many more points and, as shown here, can be used to obtain a representative, site‐integrated perspective on subsurface weathering. Our approach aggregates data from multiple seismic refraction surveys into a single frequency distribution of porosity and depth for the surveyed area. We calibrated the porosities at a site where cores are coincident with seismic refraction surveys. Modeled porosities from the survey data match measurements at the core locations but reveal a frequency distribution of porosity and depth that differs markedly from the cores. Our results highlight the value of using the site‐integrated perspective obtained from the geophysical data to quantify subsurface weathering and water‐holding capacity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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 teacher head, 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".