Impact of particle concentration and out-of-range sizes on the measurements of the LISST
Bibliographic record
Abstract
Abstract The instrument LISST (laser in situ scattering and transmissiometry) has been widely used for measuring the size of oil droplets in relation to oil spills and sediment particles. Major concerns associated with using the instrument include the impact of high concentrations and/or out-of-range particle (droplet) sizes on the LISST reading. These were evaluated experimentally in this study using monosized microsphere particles. The key findings include: (1) When high particle concentration reduced the optical transmission (OT) to below 30%, the measured peak value tended to underestimate the true peak value, and the accuracy of the LISST decreased by ~8% to ~28%. The maximum concentration to reach the 30% OT was about 50% of the theoretical values, suggesting a lower concentration level should be considered during the instrument deployment. (2) The out-of-range sizes of particles affected the LISST measurements when the sizes were close to the LISST measurement range. Fine below-range sizes primarily affected the data in the lowest two bins of the LISST with >75% of the volume at the smallest bin. Large out-of-range particles affected the sizes of the largest 8–10 bins only when very high concentration was present. The out-of-range particles slightly changed the size distribution of the in-range particles, but their concentration was conserved. An approach to interpret and quantify the effects of the out-of-range particles on the LISST measurement was proposed.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".