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A process-based model for production and evolution of sediment particles by physical and chemical weathering in mountain catchments

2020· article· en· W3083138327 on OpenAlexaff
L. S. Sklar, C. S. Riebe

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil erosion and sediment transport
Canadian institutionsConcordia University
Fundersnot available
KeywordsBedrockSedimentErosionWeatheringSoil production functionGeologySediment transportLithologyTectonicsSedimentary budgetGeomorphologyHydrology (agriculture)Deposition (geology)Soil scienceGeochemistryGeotechnical engineeringPedogenesisPaleontology

Abstract

fetched live from OpenAlex

Landscapes evolve through interactions between subsurface processes that move and deform bedrock, and surface processes that redistribute mass through erosion, transport, and deposition of sediment. Sediment is composed of discrete particles that are produced from bedrock and modified during transport by physical and chemical weathering. Sediment particle attributes, including size, angularity, and durability, therefore depend on the climatic, tectonic, and lithologic factors that regulate weathering processes. These attributes, in turn, influence rates and modes of sediment transport, and the tools and cover effects that control rates of river incision into bedrock. Thus the production of sediment helps set the slopes of river channels and the relief structure of landscapes, making it central to the feedbacks between tectonics, climate, and erosion that create topography. Despite their importance, sediment particles are rarely included explicitly in landscape evolution modeling due to gaps in understanding of sediment production on hillslopes, the particle evolution that occurs on hillslopes and in channels, and the implications of sediment attributes for river incision into bedrock. Although these processes have been studied in isolation, they have not been combined together in a comprehensive model of the role of sediment in climate-tectonic-erosion feedbacks. Here we present results from a new, spatially-explicit model that predicts the evolution of individual particle attributes, including size, angularity, and durability. The model also predicts the resulting distributions of particle attributes as sediment from different sources is mixed, and as particles evolve during transport through catchments. The model has two components. The first predicts the initial particle attributes as sediments are produced from bedrock on hillslopes. The initial particle size distribution depends on the spacing of fractures and sizes of mineral grains in crystalline rocks, and on the spacing of bedding planes and the size of cemented particles in clastic sedimentary rocks. Initial size, as well as particle angularity and durability, are also influenced by chemical weathering, which depends on the fraction of soluble minerals, the local climate (parameterized as mean temperature and precipitation), and the residence time of bedrock as it is exhumed through the hillslope weathering engine. The second model component quantifies how particles change as they are transported across hillslopes and through channel networks. Particle sizes are reduced by abrasion as a function of three factors: the potential energy lost in transport; particle angularity; and particle durability, which depends on initial rock tensile strength and subsequent loss of strength due to chemical weathering. Mass lost from abrasion of coarse particles is converted to sand and silt. Particles become less angular as a function of cumulative mass loss. However, high rates of energy loss on steep slopes cause fragmentation, which creates new coarse particles and resets particle angularity. Model relationships are parameterized using published data as well as newly acquired data from laboratory experiments and field studies in the Sierra Nevada, California. We couple the model with the saltation abrasion/bedrock river incision model to simulate evolution of river longitudinal profiles, and explore potential feedbacks between rock uplift, climate, and sediment production.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score0.194

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.023
GPT teacher head0.237
Teacher spread0.214 · 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 designSimulation or modeling
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
Published2020
Admission routes1
Has abstractyes

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