The unrepeatable river: Exploring chaotic variability in laboratory channels
Bibliographic record
Abstract
Abstract Experimental work relies on the assumption that, under controlled conditions, repeat trials will yield consistent outcomes. This assumption is crucial in geomorphology, as both a theoretical and applied science, yet it is rarely examined directly. We investigated the repeatability of channel forms by conducting 14 trials using a gravel‐bed stream table. Analyses of morphology and hydraulics using simple statistics, dimensional reduction techniques and hierarchical clustering were used to quantify similarities and differences between outcomes. Average channel form was consistent across all trials, but practically, irreducible variations in initial conditions led to divergent outcomes through chaotic dynamics. Notably, two distinct channel outcomes were observed, resembling attractor states, relating to the presence or absence of an avulsion. The results indicate that even under well‐controlled conditions, repeatability is not guaranteed in physical models. The findings prompt a re‐framing of how experimental certainty and repeatability are defined in practice. Conducting repeated trials may mitigate the confounding effects of variability, but the optimal number of trials remains unclear and is likely case‐specific. We suggest that future research incorporate repetitions and explore probabilistic approaches to presenting results. Embracing and quantifying this variability can enhance the robustness of physical models and provide deeper insights into channel dynamics.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".