The influence of schlieren on in situ optical measurements used for particle characterization
Bibliographic record
Abstract
In pycnoclines, the density differences can cause light scattering —schlieren —even though only few particulate scatterers may be present. This may pose problems for the interpretation of results obtained with instruments relying on light scattering and transmission, for example the LISST (Laser In Situ Scattering and Transmissometry) particle sizer, and various cameras. Here, the influence of schlieren on in situ forward light scattering, beam attenuation and image analysis is evaluated using a LISST‐100 and a digital floc camera. Automated image analysis routines detect schlieren as particles, causing an apparent increase in particle size and volume. Re‐analysis omitting schlieren‐affected parts of the images reveals no increase. LISST beam attenuation and Volume Scattering Function (VSF) measurements indicate that schlieren can cause increases in beam attenuation due to a marked increase in the VSF at angles smaller than ~1.5°‐2°, and falsely indicate accumulation of suspended particles in the pycnocline. Light scattering caused by density differences can also cause multiple scattering, which produces an apparent decrease in particle size derived from the LISST. Schlieren is visible in images when the buoyancy frequency exceeds ~0.12 s−1. Buoyancy frequencies above 0.025 s−1 may cause increases in beam attenuation due to scattering from the density gradients and complete extinction of beam transmission can occur at buoyancy frequencies above ~0.20 s−1. All instruments measuring light scattering can potentially be affected by density differences, and results obtained in waters where buoyancy frequencies exceeds 0.025 s−1 should be interpreted carefully.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".