Small‐Scale Riverbank Erosion Experiments in Freezing and Thawing Conditions
Bibliographic record
Abstract
Abstract Climatic warming is projected to change the duration and intensity of frozen periods in polar regions, impacting hydrology and riverbank erosion. Herein we present a series of 125 laboratory flume experiments conducted in a novel cryolab morphology facility using a small‐scale Friedkin channel. We assess the influence of discharge (flow velocity), water temperature, riverbank moisture content and temperature on riverbank erosion for varying air temperatures. The riverbank topography was quantified before and after each experiment and volumetric changes were calculated, using an array of images collected via a semi‐automatic camera and structure from motion method. Videos were used to determine bank edge retreat during the experiments. Surface flow velocities were measured using particle tracking velocimetry method. An infrared thermal camera aided understanding the temperature variations across the riverbank. A non‐linear relationship has been identified between volumetric erosion rate and air temperature, with the highest rates (at maximum up to 1.03 cm3/s) occurring at −5.2°C overnight air temperatures during highest tested discharge conditions. Erosion rates decrease when temperatures fall below or rise above −5.2°C, but increase again (at maximum up to 0.51 cm3/s) at +4.5°C. High moisture content slowed temperature propagation, caused by flowing water, through the riverbank. Erosion occurred as blocks in freezing conditions when the moisture content exceeded 18.9%, which further promoted thermo‐erosional niche development, a phenomenon observed also in polar/arctic river systems. The non‐linear dependency on air temperature highlights the importance of air temperature on erosion, with further implications for erosion with climate warming.
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 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 | 0.000 |
| 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) | 0.001 | 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".