High‐resolution imaging particle analysis of freshwater cyanobacterial blooms
Bibliographic record
Abstract
Abstract Effective assessment of the health risk of cyanobacterial blooms requires an early warning system, which enables rapid detection of species of concern and determination of whether their cell concentrations exceed advisory guidelines. Advanced digital flow cytometry using FlowCam® (Fluid Imaging Technologies) in combination with light microscopy is a solid prospect for tracking cyanobacterial communities in a timely manner. However, implementation of such a method poses several challenges for the user. We first address sample preparation, instrumentation, taxonomic enumeration, and trouble‐shooting to facilitate high throughput of analyses of water samples for total cyanobacterial cell counts and their species composition. Preservation and initial screening of samples using light microscopy to estimate community size structure are endorsed to insure their archival quality and avoid clogging of the flow cell. We show that the highest magnification (×20 objective) is needed to achieve representative total and species‐specific cell enumerations. We also report that total cyanobacterial cell counts for samples analyzed using FlowCam vs. inverted light microscopy show significant positive correlation, as do those for preserved vs. live samples. Quantification of community composition using FlowCam vs. light microscopy also shows strong concordance. Although our FlowCam method performs well in the context of the World Health Organization advisory threshold of a total cyanobacterial count of 100,000 cells mL−1, it remains a work in progress in terms of reliably automated species‐level identifications.
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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 | 0.000 |
| 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".