Distinguishing mid‐channel and bank‐attached fluvial bars by flow divergence: Implications for the interpretation of stratigraphic records
Bibliographic record
Abstract
Abstract The characterization of ancient fluvial‐channel bars informs predictions of sedimentary facies distribution, and hence they are critical for interpreting river morphodynamics through time. Within active fluvial channels, sediment storage occurs along the banks or along their axial portion, generating bank‐attached or mid‐channel bars, respectively. This study introduces the angle of flow divergence ( Δf ), measured as the angle between the azimuths of bar accretionary slopes and local dune migration, and quantitatively explores its distribution in 103 channel reaches that have a sandy bed load and clear water. This dataset includes both mid‐channel and bank‐attached bars from low‐variable peak discharge rivers of Russia and Brazil. Remotely sensed evidence shows that dune migration transverse to the dip azimuth of bar slopes ( Δf 90–110°) occurs both in mid‐channel and bank‐attached bars, contradicting the long‐standing tenet that palaeoflows nearly orthogonal to the dip azimuth of large‐scale inclined strata are indicative of sinuous, single‐thread palaeochannels. Only Δf values significantly higher (>140°) or lower (<60°) than 90° discriminate mid‐channel and bank‐attached fluvial bars. Comparisons between Δf values and bar‐accretionary styles suggest that mid‐channel bars formed by high‐peak and low‐peak variable discharge rivers tend to preserve axially and laterally accreting deposits, respectively. Nearly orthogonal divergence angles are found in bank‐attached bars irrespective of peak‐discharge variability. While stressing that analyses akin to the one presented here are to be complemented with additional sedimentological information from high‐quality outcrops, this study proposes a new, quantitative, approach to better distinguish fluvial bars, and hence possibly the planform pattern of associated channel bodies, from sedimentary successions. In particular, the data show that a reliable distinction between mid‐channel and bank‐attached bars should be based on comprehensive palaeocurrent and architectural information from various portions of preserved bar bodies, and that inferences based on limited datasets should be considered with caution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".