Bibliographic record
Abstract
In fluvial geomorphology, one of the most pervasive paradigms is that the size of the grains present in a river exercises an important effect on its character. In gravel-bed rivers, there is considerable scatter in the relations between so-called “representative grain sizes” and basic channel processes and morphologies. Under a grain size paradigm, our ability to rationalize the characteristics of a given channel and predict how it will respond to a change in conditions is limited. In this paper, I deconstruct this paradigm by exploring its historical origins in geomorphology and fluid dynamics, and identify three of its underlying premises: (1) the association between grain diameter and fluid drag derived from Nikuradse’s experiments with sand-coated surfaces; (2) the use of grain size by early process geomorphologists to describe general trends across large samples of sand-bed rivers; and (3) a classificatory approach to discerning bed structures originally developed for bed configurations found in sand-bed rivers. The conflation of sand- and gravel-bed rivers limits our ability to understand gravel-bed morphodynamics. Longstanding critique of the grain size paradigm has generated alternative ideas but, due to technological and conceptual limitations, they have remained unrealized. One such unrealized idea is the morphology-based definition of bed state – an important degree of freedom within fluvial systems, particularly in reaches where adjustments to planform are not easily achieved. By embracing recent advancements in fluid dynamics and remote sensing, I present an alternative or complementary concept of bed state based on the notion that fluvial systems act to maximize flow resistance. The proposed quantitative index represents the relative contribution of morphologic adjustments occurring at different spatial scales (discriminated using a wavelet transform) to a stable channel configuration. By explicitly acknowledging the complexity of bed adjustments we can move toward a more complete understanding of channel stability in gravel-bed rivers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".