Field tests of an improved sediment tracer including non‐intrusive measurement of burial depth
Bibliographic record
Abstract
Abstract Radio‐frequency identification (RFID) transponders are now widely used to track sediment in a variety of environments. A recent innovation placed the transponder inside of a rotating inner mechanism that is designed to minimize missed detections due to burial and shielding or ‘signal collision’ effects between tracers, while also allowing a rapid measurement of the burial depth of the particle. Here we test a developed protocol for burial depth measurement and deploy the ‘Wobblestone’ tracers in the field for the first time. Results show that new tracers can be reliably positioned in the horizontal plane (median error ± 0.03 m) and that the burial depth can be accurately measured (~0.02 m maximum error). The field study was characterized by high mobility and travel lengths, and ~20% of the tracers were buried at depths up to 0.15 m. A comparison of exponential distributions for travel length of surface deposited and buried tracers indicate that the buried tracers on average traveled farther and earlier in the flood event. Tracers that did not move were also buried at one site as a result of sediment transport from upstream. Overall the technique has great potential for characterizing vertical mixing and understanding this rarely considered control on sediment transport. © 2020 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".