Dimensions of fluvial-tidal meanders: Are they disproportionally large?
Bibliographic record
Abstract
Many of the world's major river systems seemingly have one or a few disproportionally large meanders, with tight bends, in the fluvial-tidal transition (e.g., the Thames in the UK, and the Salmon River in Canada). However, quantitative studies on meanders have so far primarily focused on rivers without tidal influence or on small tidal meanders without river inflow, providing relations between channel geometry and meander characteristics (length, amplitude, and sinuosity). Physics-based predictions of meander size and shape for the fluvialtidal transition zone remain untested for a lack of data. Therefore, it remains unclear whether the dimensions of meanders in the fluvial-tidal transition zone are indeed disproportionally large, and whether meander characteristics can be used as an indicator for tidal influence. Here, data from 823 meanders in 68 fluvial-tidal transition zones worldwide are presented that reveal broad-brush relations between channel geometry and meander dimensions. Our results show that fluvial-tidal meanders indeed become larger in the seaward direction, but the dimensions are proportional to local channel width, as in rivers. Sinuosity maxima are an exception, rather than the rule, in the fluvial-tidal transition zone. Surprisingly, the width of the upstream river correlates with estuarine channel width and tidal meander size even though river discharge constitutes only a fraction of the tidal prism. The new scaling relations can be used to constrain dimensions of rivers and estuaries and their meanders.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".