Bibliographic record
Abstract
Abstract The size of particles in a suspension can be estimated by measuring the turbidity of the suspension. Turbidity measures the attenuation of a beam of light traveling through the suspension caused by the scattering and absorption of light by the particles. The amount of scattering and absorption depends on the size of the particles and their concentration in the suspension. Most of the reported applications in the literature use turbidimetry for the estimation of the particle size distribution (PSD) or an average size for particles with diameters ranging from 0.035 to 50 µm. Turbidimetric techniques are low cost, experimentally simple, fast, nondestructive and require no calibration. They are robust methods with very good reproducibility and can be used either off‐ or on‐line, in a laboratory or a plant environment. They have been used for particle or droplet size determination in a variety of diverse systems such as latex particles, silver bromide sols and cow's milk. Most of the time common ultraviolet/visible (UV/VIS) spectrophotometers are utilized for the turbidity measurements. Certain modifications may be necessary on these instruments depending on the size of the particles. The turbidity of the suspension is measured and the size of the particles or the PSD is estimated utilizing light scattering theory. For spherical isotropic homogeneous particles and absence of multiple scattering this theory is well established. The extension to nonspherical and inhomogeneous particles is a field of active research yielding numerous publications. A controversy existed in the literature regarding the capability of turbidimetry to provide the full PSD but has now been resolved. For polydisperse suspensions, for certain combinations of particle sizes and optical properties, the method cannot provide the full PSD, but only an average of the PSD. This limitation is simply a result of the light scattering patterns and it is expected theoretically. In some cases it can be overcome by simply altering the optical properties of the suspension (i.e. choosing the proper wavelengths for the turbidity measurements or a medium with a different refractive index to suspend the particles). To make the best use of the method and interpret the results in a meaningful way one should be aware of this limitation and work with it. When this is done, excellent results can be achieved. The advantages of the method and a good knowledge of its capabilities make it a very robust choice for many applications. The numerous reports from the successful application of the method are a testimony to this.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.009 |
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".