Using citizen science to track harmful cyanobacterial blooms through crowdsourcing and crowdfunding - Adopt a Lake project
Bibliographic record
Abstract
The proliferation of waterborne cyanobacterial harmful algal blooms (CyanoHABs), some of which can produce potent toxins, poses serious risks for 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 CyanoHABs in lakes and waterways across Canada. Through both crowdsourcing and crowdfunding, Adopt a Lake aimed to document the potential presence of cyanobacteria and toxins with the assistance of citizens, altogether improving public awareness to the issue of water quality preservation. Diverse water ancillary parameters were measured, combining in situ analyses by volunteers for basic water physico-chemical parameters. Samples were sent to the laboratory for more complex analyses including nutrients analysis, multiclass cyanotoxins (microcystins, anabaenopeptins, cylindrospermopsin and anatoxins) using online SPE-UHPLC-HRMS and sequencing the 16S rRNA gene as a taxonomic marker for bacteria. Data analysis of four years of sampling revealed problematic lakes that could be used to further the study of HAB occurrence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".