Evaluation of a laser-assisted particle sizing/settling velocity determination technique
Bibliographic record
Abstract
The use of predictive models for the understanding and management of sediment and contaminant transport generally requires knowledge of particle size and settling velocity. Particle size is often obtained by direct measurements, and the settling velocities are usually predicted using the Stokes' law (or a modification thereof) for single-grained spherical particles. Such measurements and estimates are not satisfactory measures for cohesive sediments, which exist as agglomerated particles called flocs and whose behaviour is significantly different from that of the single-grained particles. Direct measurement of settling velocity and size using optical methods in settling columns has also been employed to improve these predictions; however, the subjectivity in determining which particles are in focus results in unreliable size data. An out-of-focus particle will generally possess a larger size than in reality. This paper evaluates a laser-assisted particle sizing/settling velocity determination technique's ability to eliminate the subjectivity and improve particle-sizing accuracy during settling column experiments. Although the diffraction of light by the translucent standard beads (used for evaluating the technique's accuracy for determining particle size) posed a problem, the results suggest that this technique has potential for assisting researchers to obtain the most accurate settling particle size data possible. Copyright © 2005 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 | 0.001 | 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) | 0.001 | 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 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".