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Record W3120680061 · doi:10.1130/g48191.1

Anisovolumetric weathering in granitic saprolite controlled by climate and erosion rate

2021· article· en· W3120680061 on OpenAlexaff
C. S. Riebe, Russell P. Callahan, Sarah B. Granke, Bradley J. Carr, J. L. Hayes, Marlie S. Schell, L. S. Sklar

Bibliographic record

VenueGeology · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsConcordia University
Fundersnot available
KeywordsSaproliteWeatheringSoil production functionBedrockGeologyErosionRegolithPedogenesisGeochemistryGeomorphologyEarth scienceSoil scienceSoil waterAstrobiology

Abstract

fetched live from OpenAlex

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.010
GPT teacher head0.221
Teacher spread0.212 · 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.

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

Citations25
Published2021
Admission routes1
Has abstractyes

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