Tracing cyanobacterial blooms to assess the impact of wastewaters discharges on coastal areas and lakes
Bibliographic record
Abstract
The rapid detection of cyanobacterial blooms has become an emerging and urgent need during the last years due to the increasing number of cyanobacterial harmful blooms (CHABs) all over the world.The main responsibility of this phenomenon is attributable to the nutrient enrichment, resulting from eutrophication processes of anthropogenic origin.The blooms deplete oxygen in surface waters through excessive bacterial respiration and decomposition and often release toxic substances (cyanotoxins) causing fish mortality and risks for public health.We have initiated a worldwide program for the early detection of cyanobacterial blooms using combined techniques based on chemical/biochemical analyses of samples collected on specific sites identified with remote/proximal sensing tools.Here we report our results obtained from the analysis of cyanobacterial blooms using a new powerful approach based on the combined use of LCMS/MS (Liquid Chromatography Tandem Mass Spectrometry) and Molecular Networking to detect the presence of known and novel cyanotoxins.In addition, we report the most recent results from our case studies on specific coastal areas and lakes, where the presence of cyanobacteria was confirmed to be related to the excess nutrient input of anthropogenic origin, resulting from wastewater discharges or runoff from fertilisers and manure spread on agricultural areas.The monitoring of bloom occurrence, composition, frequency and chemistry can provide important indicators of degraded water quality, supporting the Government Bodies in the evaluation of effectiveness of wastewater plans that insist on a specific coastal area.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".