The Development of Rapid Assessment Methods for Harmful Algal Bloom (HAB) Toxins Using Imaging Flow Cytometry (IFCM)
Bibliographic record
Abstract
Imaging Flow Cytometry (IFCM) combined with a molecular tagging technique called Recognition of Individual Genes by Fluorescence In-Situ Hybridization (RING-FISH) provides the ability to differentiate toxin producing cells on a rapid basis. Evaluation of the IFCM method demonstrated the ability to detect signal intensity for toxin producing cells after hybridization of RNA probes with a microcystin-synthetase gene. IFCM data show signal amplification of the probe detected mainly on the outside of the cell(s) created by the toxin, accumulating mostly on the cell surface and creating a ring-shaped (halo) pattern. Our IFCM RING-FISH method can detect and delineate between toxic and non-toxic cells when applied to the specific case of Microcystis aeruginosa targeting the microcystin - synthetase gene. As such, early differentiation and detection of toxin producing cyanobacteria using either (or both) qPCR and IFCM can provide a means to improve the management of water resources to avoid public health risks.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".