Bed Particle Displacements and Morphological Development in a Wandering Gravel‐Bed River
Bibliographic record
Abstract
Abstract Bed particles were tracked using passive integrated transponder tags in a wandering reach of the San Juan River, British Columbia, Canada, to assess particle movement around three major bars in the river. In‐channel topographic changes were monitored through repeat LiDAR surveys during this period and used in concert with the tracer data set to assess the relationship between particle displacements and changes in channel morphology, specifically, the development and re‐working of bars. This has direct implications for virtual velocity and morphologic based estimates of bedload flux, which rely on accurate estimates of the variability and magnitude of particle path lengths over time. Tracers were deployed in the river at three separate locations in the Fall of 2015, 2016, 2017, and 2018, with recovery surveys conducted during the summer low‐flow season the year after tracer deployment and multiple mobilizing events. Tracers exhibited path length distributions reflective of both morphologic controls and year to year differences related to the annual flow regime. Annual tracer transport was restricted primarily to less than one riffle‐pool‐bar unit, even during years with a greater number of peak floods and duration of competent flow. Tracer deposition and burial was focused along bar margins, particularly at or downstream of the bar apex, reflecting the downstream migration and lateral bar accretion observed on Digital Elevation Models of difference. This highlights the fundamental importance of bar development and re‐working underpinning bedload transport processes in bar‐dominated channels.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".