Promise, performance and current limitations of a magnetic Bedload Movement Detector
Bibliographic record
Abstract
Abstract Understanding fluvial bedload transport and its geomorphic implications is hampered by the paucity and low accuracy of field‐derived transport data. Tunnicliffe et al. ( Hydrological Processes, 2000; 14: 2631–2643) described the Bedload Movement Detector (BMD), a magnetic induction system for measuring bedload movement in gravel‐bed rivers based on a sensor that produces signals when its magnetic field is enhanced by a passing particle. We conducted two types of laboratory experiments to attempt to calibrate the BMD system: (1) rotating platter experiments were designed to relate sensor response curves to particle properties, and (2) flume experiments were designed to measure particle speed and permit computation of a calibrated model. Physically based relations were derived amongst particle volume, speed and magnetic content, and signal integral, amplitude and width. The calibrated relations demonstrate a basis to estimate sediment flux in gravel‐bed rivers, but they require independent knowledge of particle magnetic susceptibility. Over the course of the experiments a number of weaknesses in the sensor design and performance were identified and suggestions are made to improve the system. The high spatial and temporal resolution of data that magnetic sensors of bedload movement are likely to offer makes them valuable to consider for sediment transport measurements in both the field and laboratory. Copyright © 2009 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".