Adopt a Lake: Successfully Tracking Harmful Cyanobacterial Blooms in Canadian Surface Waters Through Citizen Science
Bibliographic record
Abstract
The proliferation of harmful waterborne cyanobacterial algal blooms, some of which can produce potent toxins, poses severe risks to environmental and human health. Academic and governmental monitoring efforts may be constrained by budget, time, and staff, and thus miss otherwise significant pollution events. Here, we report on the implementation of a citizen science project to track harmful cyanobacterial blooms in lakes and waterways across Canada. Through both crowdsourcing and crowdfunding, the Adopt a Lake (Adopt a Lake 2022) campaign aimed to document the potential presence of cyanobacteria and toxins with the assistance of participants, thus improving public awareness of the issue of water quality preservation. Using social media, participants were encouraged to participate in the initiative by collecting samples during a bloom from a nearby pond or by making a financial contribution to support the initiative. Adopt a Lake benefitted from the analytical platform of Algal Blooms Treatment, Risk Assessment, Predictions, and Prevention (ATRAPP), a research project focused on the prediction and management of harmful cyanobacterial blooms. The presence of cyanotoxins, which can confirm whether a lake has a toxic bloom, was determined through high-resolution mass spectrometry analyses. This paper presents an overview of the implementation of the Adopt a Lake initiative, the campaign’s status, and the lessons learned, and it argues the importance of continual monitoring of cyanobacterial blooms.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".