Thor: a rock strength database for investigating lithologic controls in landscape evolution
Bibliographic record
Abstract
Topographic information has been widely used to provide insights into tectonics and climate. However, these metrics are subject to lithologic effects that limit our understanding of how landscapes grow, evolve, and decay. To address this, we compile over 6100 Schmidt hammer-based rock strength measurements from 264 published studies to create an extensive rock strength database that we call Thor . We use this database to perform a meta-analysis that enables us to quantify, on a global scale, the impact of lithology on fluvial metrics. Our findings reveal a strong correlation between topographic metrics and rock strength, particularly the normalized steepness index ( k sn ) and the fluvial erodibility coefficient ( K ). Additionally, using four case studies worldwide we identify that incorporating rock strength data improves the correlation between measured and predicted erosion rates for all sites, regardless of tectonic activity and climate setting. While we acknowledge the roles of climate and tectonics in shaping Earth's surface, our findings reveal a notable influence of lithology in landscape evolution, both globally and locally. These findings underscore the potential bias introduced by spatially variable lithology on commonly used topographic metrics for inferring drainage reorganization, incision, tectonics, and climate forcing.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.025 | 0.017 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".