Modeling the Spread of Airborne Particles Associated with Harmful Algal Blooms and Plumes of Colored Smoke
Bibliographic record
Abstract
Lakes and oceans are threatened by harmful algal blooms (HABs), caused mostly by toxic cyanobacteria. When people or animals drink the toxic water, it can be damaging to their health, potentially leading to hospitalization or even death. In some cases, these toxins are not just limited to the water, but can become airborne through wave breaking, bubble bursting, and spume droplet formation. New information is needed regarding the transport and fate of HAB-associated aerosols. The overall goal of this research was to monitor particle concentrations and measure meteorological conditions near HAB sites to determine the conditions that may lead to increased exposure to HAB cells and toxins in the atmosphere. By creating predictions of which conditions and locations will be experiencing higher aerosol levels at any given time, models could be used to inform the public and policy makers to ensure that appropriate responses and safety measures can be taken. The research also includes experiments to study plumes of colored smoke, as a proxy for the transport of biological particles such as HAB cells, pollen, and pathogens. The first objective of this research was to explore associations between measured weather conditions and particle concentrations measured above active HABs and HAB sites using drone-based sensor packages. The second objective was to monitor wind and particle concentrations near freshwater and marine HABs using ground-based sensor packages. The third objective was to model HAB aerosol behavior at a beach level to predict respiratory irritation. The fourth objective was to use aerial and ground-based sensors and images of colored smoke to predict particle concentrations at different distances and intensity levels downwind from the source(s).
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".