Cyanobacterial detection using in vivo fluorescence probes: Managing interferences for improved decision‐making
Bibliographic record
Abstract
The applications of in vivo probes that can detect the fluorescence of cyanobacterial phycocyanin are emerging and widely used for cyanobacterial detection in source waters. The objectives of this project were to study the sources of interferences involved with the readings of five probes (three commercially available probes and two prototype probes) using laboratory cultures and field samples. To compare the direct readings of different probes, the probe readings were presented in the form of a biovolume equivalent of cyanobacteria. Inorganic turbidity and the presence of algal biomass interfered with probe readings. A correction factor was developed for the cyanobacteria probes using simultaneous chlorophyll a measurements. The field data demonstrate that the potential underestimation of cyanobacterial biomass that corresponds to alert levels is a major issue with the application of in vivo probes. These alert levels are used to trigger monitoring and management actions. This study shows that the correlation between a probe's reading and cell count is almost meaningless, and that the correlation to biovolume is a relevant option for management purposes. Results show that probe users should be fully aware of the sources of interferences when applying and interpreting the results. In addition, the authors offer a novel procedure that corrects for chlorophyll a interference.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".