Anisovolumetric weathering in granitic saprolite controlled by climate and erosion rate
Bibliographic record
Abstract
Abstract Erosion at Earth’s surface exposes underlying bedrock to climate-driven chemical and physical weathering, transforming it into a porous, ecosystem-sustaining substrate consisting of weathered bedrock, saprolite, and soil. Weathering in saprolite is typically quantified from bulk geochemistry assuming physical strain is negligible. However, modeling and measurements suggest that strain in saprolite may be common, and therefore anisovolumetric weathering may be widespread. To explore this possibility, we quantified the fraction of porosity produced by physical weathering, FPP, at three sites with differing climates in granitic bedrock of the Sierra Nevada, California, USA. We found that strain produces more porosity than chemical mass loss at each site, indicative of strongly anisovolumetric weathering. To expand the scope of our study, we quantified FPP using available volumetric strain and mass loss data from granitic sites spanning a broader range of climates and erosion rates. FPP in each case is ≥0.12, indicative of widespread anisovolumetric weathering. Multiple regression shows that differences in precipitation and erosion rate explain 94% of the variance in FPP and that >98% of Earth’s land surface has conditions that promote anisovolumetric weathering in granitic saprolite. Our work indicates that anisovolumetric weathering is the norm, rather than the exception, and highlights the importance of climate and erosion as drivers of subsurface physical weathering.
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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 | 0.000 | 0.001 |
| 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".