Quantitative Real-Time Polymerase Chain Reaction (PCR) Assay for Rapid Monitoring of the Harmful Algal Bloom Species Cochlodinium polykrikoides
Bibliographic record
Abstract
Harmful blooms of the dinoflagellate Cochlodinium polykrikoides (Margalefidinium polykrikoides) had detrimental aquacultural and economic effects globally, and to reduce the damage caused by these blooms, early biomonitoring and quantitative analysis of C. polykrikoides are of the utmost importance. Here, for the detection of C. polykrikoides using quantitative real-time polymerase chain reactions, we developed specific primers targeting the large subunit ribosomal DNA (LSU rDNA) and evaluated their applicability in the field during the occurrence of a C. polykrikoides bloom. The specific primers not only accurately detected C. polykirkoides but also had a detection performance comparable with that obtained using microscopic observations. Accordingly, we developed a system that can be used in the field and applied when red tides occur, with accurate results being obtained more than five times more rapidly than those obtained based on microscopic analysis. Collectively, our findings indicate that the C. polykrikoides bloom detection system developed in this study can be applied to rapidly detect and accurately quantify C. polykrikoides in environmental samples. Data obtained using this system could be used as a basis for developing prompt monitoring and warning systems for the early detection of C. polykrikoides blooms in the field.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".