Light attenuation due to preferential orientation of particles in waves and shear flow: Idealized modeling for bacteria, algae, and microplastics
Bibliographic record
Abstract
Abstract Particles are a key component of aquatic light climate due to their attenuation of light. Near the water surface, waves and sheared currents can induce a preferential orientation of nonspherical particles that alters their inherent optical properties and the associated light attenuation. This modeling study focuses on how particle shape, and the corresponding preferential orientation, impacts the light climate in an aquatic environment. We assume aquatic particles, such as bacteria, algae, and microplastic pollutants, are optically homogeneous spheroids that move with the flow. The model computes their preferential orientations within the upper water column in flow driven by linear water waves and sheared currents. This is combined with the anomalous diffraction optical approximation to examine the effect of particle orientation on the beam attenuation coefficient. We find that the preferential orientation by waves and shear tends to increase the projected area of the spheroid compared to random (isotropic) orientation. This has particle size‐dependent effects on light attenuation: for particles comparable in size and shape to algae or microplastics, the preferential orientation corresponds to an increase of 10–25% in the beam attenuation coefficient, whereas there is a decrease of 10–20% in the beam attenuation coefficient for smaller particles comparable in size to bacteria. Overall, our results reveal how preferential orientation of nonspherical particles by waves and currents can impact light climate in the upper water column.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".