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Record W6990950345

Evaluating Field Evidence of Fine-grained Sediment Abrasion in Gravel-bedded Rivers: a Case Study of the Lillooet and Suiattle Rivers in the Cascade Volcanic Arc (British Columbia, Canada, and Washington, USA)

2024· article· en· W6990950345 on OpenAlexaboutno aff

Bibliographic record

VenueBucknell Digital Commons (Bucknell University) · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological formations and processes
Canadian institutionsnot available
Fundersnot available
KeywordsRoundness (object)SphericitySedimentFluvialGranulometryAbrasion (mechanical)Grain sizeSorting
DOInot available

Abstract

fetched live from OpenAlex

Steep, gravel-bedded rivers are important sites for studying gravel abrasion, where material transported as bed load experiences size reduction and rounding due to grain-to-grain collisions. Studying fine sediment (diameter) abrasion and grain shape changes in fluvial systems is more challenging due to difficulties in quantifying sediment shape at this scale, but it is assumed that fine particles traveling in suspension do not experience abrasion. Improvements in particle characterization technology now allow for the accurate, rapid quantification of particle shape for sediments as fine as 0.8 μm through dynamic image analysis. Here we use the Camsizer X2 (CX2) to test the null hypothesis that fine-grained sediments have the same grain shapes along the length of a steep fluvial system. Our study sites are the Lillooet River (British Columbia, Canada) and the Suiattle River (Washington, USA) in the Cascade Volcanic Arc, chosen due to their known point sources of fine-grained sediments near their headwaters. Longitudinal sampling along 60 km of downstream distance (Lillooet River) and 35 km of downstream distance (Suiattle River) was completed through the collection of sediment on the downstream tail of gravel bars. Results show remarkable consistency across average values of shape parameters from all sample sites; volume-based sphericity (SPHT3) ranged from 0.822 to 0.861 (average: 0.848, standard deviation: 0.01), and volume-based Krumbein roundness ranged from 0.295 to 0.383 (average 0.347, standard deviation: 0.026). Shape data analysis shows no significant relationship between fine sediment downstream location and shape parameters in either river system. Results binned by sand size classes indicate slight differences in average form parameter values even across downstream distance; where fine sand consistently has a more spherical average form than medium sand, which in turn has a more spherical average form than coarse sand (SPHT3 of 0.865, 0.843, 0.811, respectively). Point counts of 100 grains across the three size classes for six Lillooet River samples shows a higher abundance of lithic fragments in coarser grain sizes. Thus, finer sands are more quartz-rich and rounded compared to coarser, lithic-dominated, more angular sands - contrary to all traditional, a-priori interpretations of mineral resistance to abrasion in fluvial transport. Complex interrelations between particle size, shape, and composition likely lead to this result, impacted by mechanical wear through grain breakages, hydraulic sorting, or differing source rock sizes and shapes. Overall, linear trends and consistencies across shape parameters cannot reject the null hypothesis and suggests that fine sediments do not experience abrasion in steep, gravel-bedded rivers. This result is consistent with previous studies in sandbedded rivers and supports the assumption that shape changes in sediment(Liang and Yang, 2023) shows similar form parameter values in the sand of fluvial environments derived from mountains, and a similar relationship between grain shape and size. These results and interpretations show the potential of distinguishing these fluvial environments from aeolian or beach environments through rapid, large-scale measures of grain shape.

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)0.000
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 score0.100
Threshold uncertainty score0.388

Codex and Gemma teacher scores by category

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

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 head0.034
GPT teacher head0.233
Teacher spread0.198 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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