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Record W2972764479 · doi:10.6084/m9.figshare.6447968

The influence of weathering on the spatial distribution of erodibility in bedrock river channels

2018· article· en· W2972764479 on OpenAlexaboutno aff
Charles M. Shobe

Bibliographic record

VenueOpen MIND · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicIntegrated Water Resources Management
Canadian institutionsnot available
Fundersnot available
KeywordsWeatheringBedrockGeologySoil production functionDistribution (mathematics)Hydrology (agriculture)GeomorphologySoil scienceGeotechnical engineeringPedogenesisSoil waterMathematics

Abstract

fetched live from OpenAlex

This is a presentation that was given on October 20th, 2014 at the Geological Society of America annual meeting in Vancouver, BC, Canada. The talk presents rock strength, surface roughness, and crack density data from tributaries to the Potomac River in Virginia. Our data support the idea that bedrock channel erodibility is greater on the channel margins than at the thalweg, which we hypothesize to be the result of weathering damage preferentially accumulated on the channel banks. This work was published in Shobe et al (2017; Earth Surface Processes and Landforms). Note: This presentation contains figures reproduced from Hancock et al (2011; Journal of Geophysical Research: Earth Surface) Abstract: Weathering may yield spatial variations in bedrock strength across rock-floored channel cross-sections, because weathering is dependent on geomorphic stability that varies in an active channel. Modeling results suggest that the channel erosion rate dictates the weathering variance possible in any given channel, but this has not been tested in the field. We test if average cross-section erosion rate can exert a first-order control on weathering intensity. We present data from three Potomac River tributaries undergoing transient response to ~30 m of baselevel drop following the passage of Great Falls. Erosion rates along tributary profiles vary from <0.5 to ~0.8 m/ky. We selected nine channel cross-sections: one below, within, and above the major knickzone on each tributary. Cross-sections within each tributary exhibited consistent lithology. At each section, we quantified weathering through measurements of compressive strength, surface roughness, and crack density at multiple heights above the thalweg as a proxy for degree of geomorphic stability. At each height, we 1) obtained 60 compressive strength measurements with a SilverSchmidt hammer, 2) used a Fowler contour gage and photograph analysis to quantify rock surface roughness along six roughness profiles, and 3) measured the length, width and other properties of all cracks >2 cm length in a ~3 m2 area for selected sites. All nine cross-sections showed significant declines in compressive strength (~10% to ~50%) between the thalweg and the channel margins. Six of nine cross-sections showed significant increases in surface roughness with height. We observed three to seven times more crack length per rock area in channel margins than in the thalweg, with an average difference of 0.2 ± 0.05 mm/cm2 of visible cracks. In nearly all cases, our proxies suggest an increase in weathering with height. Differences in weathering measurements between the thalweg and the channel banks in each cross-section are inversely related to cross-sectional unit stream power (R2 = .69 for compressive strength, R2 = 0.68 for crack length per area), providing evidence that erosion rate dictates the accrual of weathering effects along the channel margin. We conclude that the interaction between the rates of erosion and weathering influences the distribution of bedrock erodibility here.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.017
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.018
GPT teacher head0.250
Teacher spread0.233 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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