Detection ranges and uncertainty of passive Radio Frequency Identification (RFID) transponders for sediment tracking in gravel rivers and coastal environments
Bibliographic record
Abstract
ABSTRACT Since the earliest use of this technology, a growing number of researchers have employed passive Radio Frequency Identification (RFID) transponders to track sediment transport in gravel rivers and coastal environments. RFID transponders are advantageous because they are inexpensive, durable and use unique codes that allow sediment particle mobility and displacement to be assessed on a clast‐by‐clast basis. Despite these advantages, this technology is in need of a rigorous error and detection analysis. Many studies work with a precision of ~1 m, which is insufficient for some applications, and signal shadowing can occur due to clustering of tagged particles. Information on in‐field performance is also incomplete with respect to burial and submergence, especially for different transponders and antennae combinations. The objectives of this study are to qualify and quantify the factors that influence the detection zone of RFID tracers including antenna type, transponder size, transponder orientation, burial depth, submergence and clustering. Results of this study show that the detection zone is complex in shape due to a set of lobes in the detection field and provide a better understanding of transponder detection shape for different RFID transponder/antenna combinations. This study highlights a strong influence of clustering and submergence, but no significant effect of burial. Finally we propose standard operating procedures for tagging and tracking in rivers and coastal environments. Copyright © 2014 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 | 0.003 | 0.008 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".