Bio-tracking, bio-monitoring and bio-magnification interdisciplinary studies to assess cyanobacterial harmful algal blooms (cyanoHABs)’ impact in complex coastal systems
Bibliographic record
Abstract
Cyanobacterial Harmful Algal Blooms (cyanoHABs) represent significant threats to human health and environmental sustainability. These blooms, characterized by the rapid proliferation of toxic species, can release harmful toxins into aquatic environments, with severe consequences for ecosystems and human populations. Traditional research on cyanoHABs faces several limitations, including the lack of standardized detection methods, environmental variability, and low awareness of the associated risks. Most studies rely on conventional laboratory techniques, which are often resource-intensive and not widely accessible. Additionally, the complex dynamics of cyanoHABs, influenced by factors such as temperature, nutrients, and bloom evolution, make it difficult to establish consistent regulatory and monitoring frameworks. This paper presents a new integrated strategy that combines advanced technologies (remote sensing, in-situ multispectral analysis, mass spectrometry) with bio-monitoring and bio-tracking. This interdisciplinary approach improves the monitoring of cyanoHAB spread, tracks bioaccumulation in the food chain, and provides timely warnings for public health protection. The case study focuses on the Campi Flegrei area, an active volcanic region in Southern Italy, where Lake Avernus, a volcanic lake, has experienced periodic cyanobacterial blooms. This region also hosts mussel aquaculture and recreational activities. Remote sensing allowed the tracking of the 2022 bloom from the lake to the sea, reaching a mussel farm along the coast. Rapid detection and quantification of anabaenopeptins in bivalves enabled timely alerts to local authorities, prompting an assessment of contamination risks. The study demonstrates how the integration of remote sensing and molecular analysis enhances environmental monitoring by providing real-time, high-resolution data. This approach supports a better understanding of bloom dynamics, bioaccumulation, and impacts on the food chain, informing risk management and regulatory strategies. The research highlights the value of combining advanced technologies to improve the management of cyanoHAB-related risks, protecting both human health and ecosystem sustainability.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".