An Autonomous Platform for Near Real-Time Surveillance of Harmful Algae and Their Toxins in Dynamic Coastal Shelf Environments
Bibliographic record
Abstract
Efforts to identify in situ the mechanisms underpinning the response of harmful algae to climate change demand frequent observations in dynamic and often difficult to access marine and freshwater environments. Increasingly, resource managers and researchers are looking to fill this data gap using unmanned systems. In this study we integrated the Environmental Sample Processor (ESP) into an autonomous platform to provide near real-time surveillance of harmful algae and the toxin domoic acid on the Washington State continental shelf over a three-year period (2016–2018). The ESP mooring design accommodated the necessary subsystems to sustain ESP operations, supporting deployment durations of up to 7.5 weeks. The combination of ESP observations and a suite of contextual measurements from the ESP mooring and a nearby surface buoy permitted an investigation into toxic Pseudo-nitzschia spp. bloom dynamics. Preliminary findings suggest a connection between bloom formation and nutrient availability that is modulated by wind-forced coastal-trapped waves. In addition, high concentrations of Pseudo-nitzschia spp. and elevated levels of domoic acid observed at the ESP mooring location were not necessarily associated with the advection of water from known bloom initiation sites. Such insights, made possible by this autonomous technology, enable the formulation of testable hypotheses on climate-driven changes in HAB dynamics that can be investigated during future deployments.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".