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. \nThe 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.\nThe second objective was to monitor wind and particle concentrations near freshwater and marine HABs using ground-based sensor packages. \nThe third objective was to model HAB aerosol behavior at a beach level to predict respiratory irritation.\nThe 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".