Assessing the Performance of In-Stream Restoration Projects Using Radio Frequency Identification (RFID) Transponders
Bibliographic record
Abstract
Instream channel restoration is a common practice in river engineering that presents a challenge for research. One research gap is the development of monitoring techniques that allow for testable predictions of sediment transport and supply. Here we use Radio Frequency Identification (RFID) transponders to compare the short-term (1-year) sediment transport response to flood events in a restored and a control reach. The field site is Wilket Creek, an enlarged creek in a fully urbanized catchment without stormwater management control in Toronto, Ontario. The responses to three flooding periods, each of which are at or above the design bankfull discharge, are described. Key results are that (i) particle mobility is lower in the restored reach for all three periods; (ii) full mobility occurs in the control reach during the first two floods while partial mobility occurs in the restored reach; and (iii) the constructed morphology exerted a controlling influence on particle entrainment, with higher mobility in the pools. Log-transformed travel distances exhibit normal distributions when grouped by particle size class, which allows a statistical comparison with power law and other predictive travel-distance relations. Results show that three bedload transport conditions can occur, with partial mobility associated with a mild relation between particle size and travel distance and full mobility associated with either a flat or steep relation depending on the degree of integration of particles in the bed. Recommendations on seeding strategy and sample sizes are made to improve the precision of the results by minimizing confidence intervals for mobility and travel distances. Even in a short term study, the RFID sediment tracking technique allows a process-based assessment of stream restoration outcomes that can be used to justify the instream intervention and plan future attempts to stabilize and enhance the system.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".