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Record W4406728121 · doi:10.1126/sciadv.adr2610

Rock strength controls erosion in tectonically dead landscapes

2025· article· en· W4406728121 on OpenAlexaff
Maurício Barcelos Haag, Lindsay M. Schoenbohm, Joshua Wolpert, Scott Jess, Paul R. Bierman, Lee B. Corbett, Carlos Augusto Sommer, Gabriel Endrizzi

Bibliographic record

VenueScience Advances · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLithologyGeologyBedrockTectonicsErosionGeomorphologyPhysical geographyEarth sciencePaleontology

Abstract

fetched live from OpenAlex

Interactions among tectonics, climate, and lithology shape the Earth's surface. In regions dominated by tectonic quiescence and climate stability, the role of rock strength related to lithology, and its role in landscape evolution, can be most clearly perceived. We leverage these qualities in a unique field site by integrating topographic data, erosion rates, and a large suite of rock strength measurements to quantify the relationship between bedrock strength and erosion rates along a 200-km section of the southeast coast of Brazil where climatic and tectonic variability are minimal. Our findings reveal a 20-fold erosion rate variation best explained by differences in rock strength. We also demonstrate that incorporating lithological strength variability into the analysis of landscape incision substantially improves accuracy, with outcomes that better reproduce natural settings. Our results underscore the crucial role of rock strength in landscape evolution and emphasize the need for field studies to account for lithological variability and thus accurately interpret landscape dynamics.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.871

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.009
GPT teacher head0.277
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2025
Admission routes1
Has abstractyes

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