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Record W2110536870 · doi:10.2110/jsr.2011.69

Evidence of Bedform Superimposition and Flow Unsteadiness In Unit-Bar Deposits, South Saskatchewan River, Canada

2011· article· en· W2110536870 on OpenAlexaboutno aff
Arnold Jan H. Reesink, John Bridge

Bibliographic record

VenueJournal of Sedimentary Research · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsnot available
Fundersnot available
KeywordsGeologyBedformSuperimpositionGeomorphologyUnit (ring theory)Bar (unit)GeochemistryOceanographySediment transportSediment

Abstract

fetched live from OpenAlex

Abstract: Unit-bar deposits are ubiquitous components of river-channel deposits and strongly influence their hydrological properties, yet they are not easy to interpret. This paper concerns details of the internal structures of six unit bars from the South Saskatchewan River, Canada, that were investigated using trenches, epoxy resin peels, and 900 MHz ground-penetrating radar (GPR) profiles. The composition of unit bars depends on flow unsteadiness and superimposed bedforms. Flow unsteadiness causes changes in the mean grain size of the sediment in transport, but is expressed primarily as a change in the type and direction of migration of smaller ripple- and dune-scale bedforms superimposed on unit bars. Superimposed bedforms with heights that exceed 25% of the host bedform height reduce their host's slope and generate inclined sets. Host bedforms with smaller superimposed bedforms form angle-of-repose cross strata with a visible pre-sorting pattern. The relationship between the formative host- and superimposed bedforms and such pre-sorted cross strata can be used to interpret numerous aspects of the three-dimensional geometries of the bedforms. Such detailed interpretations rely on: (i) regularly spaced fine-grained drapes deposited during the passage of troughs of superimposed bedforms, (ii) grain-size sorting patterns generated by sorting within the superimposed bedforms before deposition, (iii) grain-size sorting during deposition by grainfall and intermittent grainflows down the bar lee-slope, and (iv) the presence and nature of low-angle reactivation surfaces. The detailed interpretations revealed that the deposits of dam-related floods were significantly smaller than the deposits of individual unit bars in this study. The unsteady flow and sediment transport conditions are reflected by changes in the structural composition of the unit bars. Reactivation surfaces associated with flow unsteadiness, as opposed to those formed by large superimposed bedforms, were characterized by changes in mean grain size, the buildup of sediment at the base of the bar lee slope, and changes in structures formed by superimposed bedforms. Reactivation surfaces generated by large superimposed bedforms indicate that bedform preservation is likely increased in areas of flow deceleration. The trough deposits of exposed unit bars contained bubble sand, planar laminae due to upper-stage plane beds, and low-angle inclined laminae due to antidunes. These structures indicate that unit-bar troughs can act as ephemeral channels. The composition of the investigated unit bars thus emphasizes the interplay between flow unsteadiness and bed morphology in the formation of sedimentary structures in river channels. Sediment surfaces generate visible GPR reflections where the thickness and contrast in electrical properties of the sediment layers are sufficiently large. Most cross-stratified sets were represented only by reflections from the base of the set, and not by high-angle inclined reflections from the cross strata. The local absence of reflections from cross strata is attributed to the lack of contrast in electrical properties of the well-sorted cross strata and their limited thickness relative to the radar wavelength. In contrast to cross strata, reactivation surfaces formed by large superimposed bedforms and flow unsteadiness were commonly associated with distinct inclined reflections.

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 score0.083
Threshold uncertainty score0.920

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 communication0.0000.001
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 head0.063
GPT teacher head0.283
Teacher spread0.220 · 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

Citations64
Published2011
Admission routes1
Has abstractyes

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